U.O.No. 8058/2014/Admn Dated, Calicut University.P.O, 14.08.2014 Muhammed S Deputy Registrar Forwarded / By Order Section Officer File Ref.No.42471/GA - IV - E2/2013/CU UNIVERSITY OF CALICUT Abstract Faculty of Engineering - Board of Studies in Engineering(PG) - Syllabus - M.Tech Course - Machine Learning - with effect from 2014 admission - Approved - Sanctioned - Orders issued. G & A - IV - E Read:-1. U.O. No. 4126/2014/Admn dated 25-04-2014 2. Item No. III(a) of the Minutes of the meeting of the Board of Studies in Engineering(PG) held on 19-06-2014. 3. Item No. 2 of the minutes of the meeting of the Faculty of Engineering held on 25-06-2014. ORDER Vide paper read as 1 st , an Expert Committee was constituted to frame the syllabus for M.Tech Programme in Machine Learning in tune with the M.Tech Regulation -2010 of this University. Vide paper read as 2 nd above, the Board of Studies in Engineering (PG) held on 19-06-2014, resolved to approve the Syllabus of M.Tech Programme in Machine Learning, submitted by the Expert Committee, fixing the Eligibility Criteria for the admission to the Course to be B.Tech Degree in Computer science/Electronics and Communication Engg/ Instrumentation/ Electronics/Electronics and Instrumentation/ Applied Electronics and Instrumentation Engg /Electrical and Electronics/or equivalent. The meeting of the Faculty of Engineering held on 25-06-2014, vide item No. 2 of its minutes, resoved to approve the minutes of the meeting of the Board of Studies in Engineering(PG) held on 19-06-2014. The Hon'ble Vice Chancellor, having considered the exigency of the matter in detail has ordered to implement the syllabus for M.Tech Programme in Machine Learning, subject to ratification by the Academic Council. Sanction has therefore been accorded for implementing the syllabus of M.Tech Course in Machine Learning with eligibility criteria for admission to the course to be B.Tech Degree in Computer science/Electronics and Communication Engg/ Instrumentation/ Electronics/Electronics and Instrumentation/ Applied electronics and Instrumentation Engg /Electrical and Electronics/or equivalent, subject to ratification by the Academic Council Orders are issued accordingly. Syllabus appended. To Principals of all affiliated Engineering Colleges Copy to : - PS to VC/PA to PVC/ PA to Registrar/PA to CE/ DR/AR M.Tech/ CDC / Dean, Faculty of Engineering/ Chairman, BS in Engineering/ PRO/ Enquiry Section/SA( to upload in the University website)
MMLIo 302 (C) Neural Networks for Machine Learning
Note 7 i 50 percent of marl<s for MMLIO SU (P) $ould, be evalaated by Supervisor
anil the remaining by Evaluation Committee.
Note 2: The student has to anfuruke deparntentalwork assignedby HoD.
slNo
Course Code Subject L T P ICA ESE Total Credits
1 MML10 301 Elective IV 3 L 0 100 100 200 4
2 MML10 302 Elective V 3 1 0 100 100 ,nn 4
3 MML1o 303 Industrial Training 0 0 0 50 50 1
4 MMLIo 304 (P) Master ResearchProject Phase 1
0 0 300 300 6
Total 06 02 550 200 750 15
Semester 4
Total credits for all semesters : 75
Note 1 : 50 percent of lcA mdr|s for MMLI| 401 (l) woald be evalwted by Supervinrand the remaining by Evaluation committee.
Note 2 : 50 percent of EiEmqrks for Mwlo 401 (P) wouldbe evaluatedby rxtcmalEvaluator and the remaining thtoagh viva voce.
Note 3 : The sudenthas a undertake clepartmenalwork assigned,by HoD.
slNo
Course Code Subject L T P ICA ESE Total credits
1 MMLl0 401(P)
Master ResearchProject Phase z
0 0 30 300 300 600 t2
Total 0 0 30 300 300 600 72
Detailed Syllabus for M Tech in Machine Learning
MML1o lO1Probability and statistics
Hours/week :
Lecture - 3 and Tutorial - 1credits : 4
Objectives: To infro duce concepts of probability anil statistics ond. their applicatiota.
Module I : Probability (rl hrs) - Probability Distributions, Binomial, Poisson, Normal,Uniform,Geometric and Hyper geometric, Exponential, Weibull, Beta, Gamn4 Joint distribution
Module II : Sampling distribution (ts Hrs) - Sampling distribution of mean and variance, Testing ofhypothesis, Estimation, Method of least squares, Fitting of straight line, Fitting of second degree
curve, Correlation and regression, Fisher's transformation.
Module III : Stochastic Process (tg ftrs) - examples, Specifications of Stochastic process, Stationaryprocess, Markov chains, Transition Matrix, Higher transition probabilities, Classifications of states
and chains, Images as a Stochastic process.
Module IV : Design of experiments (t: Hrs) - Analysis ofvariance, Statistical principles ofexperimentation, completely randomized designs, Randomized block desiSrs, Multiple comparisons,some further experimental designs, Analysis of covariance.
References:
l. Miller and Freund" Probdbiliy o0td, stctistics, Pearson, 2o1o
2. Gupta and Kapo or, Futrdatnertab ofMsthematkil Statistr'cs. Sr"rltan Chand and Sons,
2012
3.J. Medhi, Stochasfilc Process, 3rd E4 New Age science 2oo9
4. Harry Frank and Steven C, St4tistics conccpu andapplications. Cambridge University Press,
L994
5. S.M Rose, Introd uction to Probability and statistics for Engineers and scientists, tth Ed, Elsevier,2009
6. Sonka, V Hlavac and Roger Boyle,lmry Processing, Analysis mdMachiae Vision, Cengage
Learning 200E
Internal continuous assessment (IcA) : 100 marlGInternal continuous assessment is in the form of periodical tests, assignments, seminars or a
combination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1OO marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20marksQuestion4:20marlc
Module IIIQuestion5:20marksQuestion6:20mark
Module IVQuestionT:20marksQuestionS:20marlG
MML10 102Linear AlgebraHours/week :
Lecture - 3 and Tutorial - ICredits : 4
Objectives: Linear Algebra is aprerequisite for aproper understanding of Machine Leaming as it b heavilydeployed in applicatiots. Thb course is to introdace the concepts oflinear algebro
Module I (r4 hrs) Matrices and Gaussian Elimination - geometry oflinear equations, example ofgaussian elimination, matrix notation, matrix multiplication, triangular factors, row exchanges,inverses, transposes, special matrices and applications. Vector Spaces - vector spaces and subspaces,linear independence, basis and dimension, four fundamental subspaces, graphs and networks, lineartransformations.
Module II (rn hrs) Orthogonality - orthogonal vectors and subspaces, cosines and projections ontolines, projections and least squares, orthogonal bases and Gram-Schmid! Fast fourier transform.Determinants - Properties, formulas and applications.
Module III (r3 hrs) Eigenvalues and Eigenvectors - diagonalisation ofa matrix, difference equationsand powers, differential equations, complex matrices, similarity transformations. Positive DefiniteMatrices - minima, maxima, saddle poinLs, tests for positive definiteness, singular valuedecomposition, minimum principles, ff nite element method.
Module IV (12 hrs) Computation with Matrices - Matrix Norm and condition number, computationof eigenvalues, iterative methods. Linear Programming and Game Theory - Linear inequalities,Simplex method, Oual problem, Network models, Game theory.
Text Books:1. Gilbert Strang, I inear Algebra and its Applicatiorls, Fourth Edn, Thomson Learning Inc, 2006
References:
1. Steven C Chapra, Applied,Numericd Methodr, 3rd Edition, Mcgraw Hill 2012
2. Jim Hefferon, Linear Algebrq SMC, Colchester, 2013
3. Jie Chen, Nume ical Linear AlgebraTechniques for Effective Data Analysis, PhD DissertationThesis, Universi$r of Minnesota, 2o1o
Internal continuous assessment (ICA) : 1fi) marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minirnum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1fi) marks
question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marlGQuestion 2:20marlc
Module IIQuestion3:20marlcQuestion4:20marlc
Module IIIQuestion5:20marksQuestion6:20marks
Module tVQuestionT:20marl6questionS:20marks
Pattern RecognitionHours/week :
Lecture - 3 and Tutorial - 1
objectives: To introd uce the theoretical and practical aspects of pattem recognition and. clustering techniques
uodule I (rzhrs) tntroduction - ifltroduction to statistical - syrtactic and descriptive approaches -features and feature extraction - learning - Bayes Decision theory - introduction - continuous case - z- category classification - minimum error rate classification - classi-Hers - Discriminant Functions -and decision surfaces - error probabilities and integrals - normal density - Discriminant functions fornormal density
Module rI (12 hours) parameter estimation and supervised learning - maximum likelihoodestimation - the Bayes classifier - learning the mean ofa normal densigr - general Bayesian learning -nonparametric technique - densigr estimation - parzen windows - k-nearest neighbour estimation _
Module III (13 hours) Machine Learning vrith support vector Machines - Introduction,supervisedautomatic learning - probabilistic Eramework Learning Algorithms- Basic concepts, Main classes ofsupervised Learning Algorithms, Bias variance Tradeoff, variance Reduction Teihniques, KernelMethods and the Evolution of svM. support vector Machines - A new avatar of kernel methods, svMformulation and Computation.
Module w (r: hours) Kernel based methods for clustering data , Introduction , x-Means clustering,Algorithm, Kernel K-Means clustering methods, spectral clustering, clustering High-DimensionalData.
Text Books:1. Duda & Hart P.E , Pattern Classification And. Scene Analysr3, John Wiley
References:
1. Dr. K. p. Soman, Ajav. V, Loganathan R.,.,Machine Leaming with SVM and other ytemel
Me thods", P rcntice-Hall India
2. Jiawei Han and Micheline Kamber, D ata Mining: concspts and Teclmiqaes , second Edition ,Universigr of lllinois at Urbana-Champaign3. Rajjan Shinghal , Pattern Recognitiorr Techniqu* and. Applications, Oxford,University Press, 2006.,
Internal continuous assessment (ICA) : 1oo markslnternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the begiruring of the semester by theteacher.
End semester Examination (ESE) : 1OO marks
question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20marksQuestion4:20mark
Module IIIQuestion5:20marksQuestion6:20mark
Module tVquestionT:20marksquestion8:20marks
Image ProcessingHours/week :
Lecture - 3 andTutorial - 1
Common with ESP10 105(C
objectives: Vbual information plrys an important role tn oJmost all areas of ow life. This course introdtcesthe fundamentals of digital image processing. It emphasizes generd pincipbs of image processiag, rather thanspecific applicatioru. It cover topics such as image representatio4 cnlor representqtions, sanpling and,quafiizatiot\ point oryrations, linear image filtering snd cotelatiory trmsforms and sabband ilecomposttions,arul nonlineor filtering, controst anil cobr enhtncemml dithering, and image restoration td. comprcssaotl Italso introduces the basic concepts ofvidro processing.
Module I (u hrs) Cray scale and colour Images, image sampling and quantization. Two dimensionalorthogonal transforms: DFf, WHT, Haar transform, KLT, DCT, lmage enhancement - filters in spatialand frequency domains, histogram-based processing, homomorphic liltering. Edge detection - nonparametric and model based approaches, LoG fflters, localisation problem.
Module II (14 hrs) Degradation Models, PsF, circulant and block - circulant matrices, deconvolution,restoration using inverse filtering, Wiener ffltering and maximum entropy-based methods ImageSegmentation: Pixel classification, Bi-level thresholding, Multi-level thresholding, p-tile method,Adaptive thresholding, Spectral & spatial classilication, Edge detection, Hough transform, Regiongrowing.
Module UI (14 hrs) Fundamental Concepts of lmage Compression: Compression models -Information theoretic perspective - Fundamental coding theorem - Lossless Compression: HufftnanCoding- Arithmetic coding - Bit plane coding - Run length coding - Lossy compression: Transformcoding - lmage compression standards.
l,todule W (fo hrs) Video Processing: Representation ofDigital video, Spatio-temporal sampling;Motion Estimation; Video Filtering; Video Compression, Video coding standards.
References:
1. A. K.Jain, Fundam entab of digtnlimage processing, Ptentice Hall oflndia, 1989.
2. R. C. Gonzalez, R. E. Woods, Digital lmage Processing, Pearson Education. II Ed.,
2002
3. W. K. Pratt, Digitdl image processinq, Prentice Hall, 1989
4. A. Rosenfeld and A. C. Kak, Dgttcl lnwge Processing,Yols. 1 and 2, Prentice Hall,1986.
5. H. C. Andrews and B. R. Hunt, Digitcl Image Pcstoratioa, Prentice Hall, 1977
6. R. Jain, R. Katuri and B.G, Schunck, Mqchine visiol,Mccraw-Hill, 1995
7. A. M, Tekalp, DigitalVid.eo Processing , Prentice-Hall, 1995
A. A. Bovik,llandbookoflmage & Video Processing, Academic Press, 2OOO
Internd continuous assessment (ICA) : 10O marksInternal continuous assessment is in the form of periodical tests, assitnments, seminars or acombination of all whichever suits best. There will be a minimun of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1O0 marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20markQuestion2:20mark
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20mark
Module IVQuestionT:20marlaQuestionS:20marks
ELECTIVES - SEMESTER I
MMLro 105 (A)Numerical Methods
Hours/week :
Lecture - 3 andTutorial - I
Objectives: To introd.uce the concepts of numeico.l methods aad their applications.
Module I (t3 Hrs) Errors in numerical calculations, significant digits, numerical solution ofpolynomial and transcendental equatiorx, bisection method, negula-falsi method Newton-Raphsonmethod, Fixed point iteration metho4 rates ofconvergence ofthese methods, solution ofsystlm ofalgebraic equations, Gauss-Jordan method, Gauss-seidel method, Relaxation methods.
Module II (rl Hrs) nolynomial interpolation, lagrange interpolation pol)momial, divided difference,Newton's divided differences interpolating polynomial, op€rators, Newton,s forward and backwarddifference interpolation polynomial, central differences, stirling's interpolation formulae.
Module III (13 Hrs) Numerical solutions of ordinary differential equations-Taylor series method,Euler's method, Modiffed Euler's method, picard's iteration method, Runge-Kutta method, Multistepmethods, Milne's predictor -corrector formulae.
Module Iv (t3 Hrs) Numerical solution ofpartial differential equations-ffnite differenceapproximation to derivatives, Crank-Nicholson met}o4 Successive over relaxation method, Finiteelement method, Galerkin's method.
References:
1, Sastry S.S., Numerical Analysis,prentice Hall India, 20122. MKJain, RKJain et al, Numertcal Methoils for scimtific and Engineering cortpurdrion New AgeInternational Publishers, 2Oo4
3. Gerald, Applied Namerical enalysis, pearson Education, 2oo44. HK Dass, Advanced,Engitueing Uathemattcs, S ChaJ.d and Co, z'tz5. Gourdin, Applied Namerical Methods, prentice Hall ofIndia,2OO4
Internal continuous assessment (ICA) : tOO marlGInternal continuolrs assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (fSf) : IOO marks
Question pattemAnswer any 5 questions by choosing at least one question from each modu.le.
Module IQuestion 1 ; 20 marksQuestion2:20marks
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20marks
Module IVquestionT:20marksQuestionS:20marks
MML10 r05 (B)Advanced Digital Signal processing
Hours/week :Lechrre - 3 and Tutorial - I
objectives: The Pu rpose of this course is to proide in-depth rleafinent on methods and. techniques in dboete-time signal trarcforms, Aigital filter design, optinat fihering and Wwer spectrum estimatiotl
Module I (rr hrs) wiener xhintchine relation - power spectral densiry - Iiltering random process,spectral Factorization Theorem, special grpes ofrandom process - signal -od"iiog - L""risq.r""".method, Pade approximation, Prony's method, iterative Prefiltering, Finite Data reiords, stochasticModels.
Module II (t: hrs) Non-parametric methods - correration method - covariance estimator -Performance ana\rsis of estimators - unbiased consistent estimators - periodogram estimator -Bartlett spectrum estimation - welch estimation - Model based approach - en, tua, enue signalmodeling - Parameter estimation using yule-Walker method,
Module III (r: hrs) Iraaximum likelihood criterion - Efffcienry of estimator - Least mean squarederror criterion - wiener filter - Discrete wiener Hopfequations - Recursive estimators - Kalnan fflter- Linear prediction, prediction emor - whitening filter, Inverse filter - Levinson recursion, Iatticerealization, Levinson recursion algorithm for solving Toeplitz system ofequations.
1. Monson H. Hayes,,,Statistical Digital Signal processing and, Modeling,,,lohn Wileyand Sons Inc., New york, 2006.
2. SophoclesJ. Orfanifis,',Optrmum Signal proeessing,,, Mccraw_Hill, 2ooo.3, John c. proakis, Dimitris G. Manolakis, ,,Diital
Sign,.l noces,sing,,, prentice Hallof India, New Delhi,2oo5.
4. Simon Haykin , " Adaptive Filter Theory", prentice Hall, Englewood Cliffs, \J 1986.5. s. Kay," M*lem specval Estimation theory ud appricatiin", prentice itail, Engrewoodcliffs, I{J re88.
Internal continuous assessment (ICA) : lOO marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tess per subject. Theassessment details are to be announced to sfudents, right at the beginning of the semester by theteacher.
End semester Eramination (ESE) : tOO marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20marks
Module IVQuestionT:20marksQuestionS:20marks
MML10 105 (C)Optimization Tecbniques
Hours/week :
Lecture - 3 and Tutorial - 1
Objectives: To apply differmt optimization techniques tobothlinear and.nonlircar systems
Module I (ra hrs) I-inear programming: statement and classiffcation of optimization problems -overview of optimization techniques - standard form of linear programming problems - Definitionsand theorems - simplex method - Revised simplex method - Duality and Dual simplex method -Sensitivity analysis.
Module II (14 }rrs) Unconstrained dimensional optimization tech-niques: Necessary and sufficientconditions - search methods(unrestricted Fibonacci and golden) - Interpolation methods(euadratic,cubic and direct root metlrod). Direct search methods - Random search - pattern search andRosenbrock's hill climbing method -Descent methods - Steepest descent, conjugate gradient, euasiNewton and DFE method.
Module III (tl hrs) Constrained optimization techniques & dynamic programming : Necessary andsufficient conditions - Equality and inequality constraints - Kuhn-Tucker conditions - Gradientprojection method - cutting plane method - Penalty function method (Interior and exterior).Principle of optimality - recurrence relation - Computation procedure - continuous dynmicprogramming.
7. Marco Dorigo, Vittorio Minizza and Alberto Colorni, "Ant System:Optimirution
by a colony of Caperation Agent" ,lEEl,Transactions on System man and Cybernetics -
Part B: Cybernetics, Vol 26, No 1, pp. zg-4t,tgg6.8. Shi, Y. Eberhart,R.C., "A ModifiedParticle Swum Optimizer", proceedings of the IEEE
International conference on Evolutionary Computation, Anchorage, AK,pp.69-73,May 1998
9. Recent literature should also be referred
Internal continuous assessment (ICA) : 1OO marksIntemal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : loo marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marl$
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20mark
Module IVQuestionT:20marksQuestionS:20marks
MML1o los (D)Information Theory and tearning Algorithms
Hours/week :
Lectue - 3 and Tutorial - 1
Objectives: To infrodace rrtncepts of informatbn theory and.leaming algorithms
Module I (re hrs) tnformation-Entropy, Information rate, classification ofcodes, Kraft McMillaninequality, source coding theorem, shannon-Fano coding, Huffrnan coding, Extended Huffinancoding -Joint and conditional entropies, Mutual information - Discrete memoryless channels - BSC,BEC - Channel capaci$r, Shannon limit.
Module II (rr hrs) namming weight, Hamming distance, Minimum distance decoding - single paritycodes, Hamming codes, Repetition codes - Linear block codes - cyctc codes - syndrome calculation,Encoder and decoder - CRC, Convolutional codes - code tree, treLlis, state diagram.
Module III (r: hrs) sequential search and viterbi algorithm - principle ofTurbo coding - AdaptiveHuffinan Coding, Arithmetic Coding
Text Books:1. David J.C. MacK ay - ffirmation Theory,Inference,atil Leaming Algorithms, CambridgeUniversity Press, 2003
2.David,Kaye- Information Theory and Machine Leanlng, phD Dissertation Thesis, DurhamUniversity 2008
3. Shu Lin & Costello D J -Error Control Coding, University ofCalifornia press, zo134. Simon Haykin - Communication Sysren, Wiley publishers, 2Oo9
5. Sam Shanmugam - Digitdl and Analog Communication,Wiley publishers 1929
Internal continuous assessnent (ICA) : 1OO markslnternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
Credits : 4
End semester Examination (ESE) : l(x) marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20marksQuestion4:20marks
Module IuQuestion5:20marksQuestion6:20marl6
Module IVquestionT:20marksQuestion8:20marks
MML10 los (E)Artifi cial Intelligence
Hours/week :
Lecture - 3 and Tutorial - 1
Credits : I
Qbjedives: Leorn the bosic lotowledge reptesentataon, problem solving, andleaming methods of Artificiallntelligence.Ass*s the applicability, strengtls, and wealovsxs of the basic lotowledge representation, problansolring, and.leaming methods in soling particttlar mgineering problems
Module I (14 hrs) Artificial Intelligence: History and Applications, production Systems, Structuresand strategies for state space search- Data driven and goal driven search, Depth First and BreadthFirst Search, DES with lterative Deepenin& Heuristic Search- Best First Search, A* Algorithm, AO+Algorithm, Constraint Satisfaction, Using heuristics in games-Minimax Search, Alpha BetaProcedure.
Module Ir (u hrs) Knowledge representation - Propositional calculus, Predicate Calculus, Theoremproving by Resolution, Answer Extraction, AI Representational Schemes- Semantic Nets, ConceptualDependenry, Scripts, Frames, Introduction to Agent based problem solving.
Module III (fZ hrs) t fachine fearning- Symbol based and Connectionist, Social and Emergentmodels oflearning, The Genetic Algorithm- Genetic Programming, Overview of Expert SystemTechnologr- Rule based Expert Systems, Introduction to Natural Ianguage processing
Module IV (12 hrs) Languages and Programming Techniques forAI- Introduction to pROLOG andLISP, Search strategies and Logic Programming in LISP, Production System examples in pROLOG
Text Books:
1. George F. Luger, "Artificial lntelligence - Stracturcs and, Strategies for Complex Problem Soling",4th Edition, Pearson Education, 2003.
References:
1. Knight, "Amficiallnullgence", Tata McGraw Hill
2, Russell & Norvig , " Affiial lntelligence : A Moilem Approcch" second edition , PearsonEducation , 2003.
Internal continuous assessment (ICA) : 1OO marksIntemal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (EsE) : 1oo marks
Question patterr
Answer any 5 questions by choosing at least one question from each module.
Module IQuestion 1 : 20 markQuestion2:20marlc
uodule IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20markQuestion6:20marks
Module IVQuestionT:20marksquestionS:20marlc
MMLIo 106 (P)Seminar I
Hours/week :
2 hrs practicalcredits : 2
Objectives: To im@ necessqry shlb to rte sa ent to present a novel id.ea on any topic related. to machineleaming, based, on own understanding of the work and existing literdture.
Based on understanding ofthe student on machine learning, each student is expected to design andexecute a Practical experiment or demonstration involving some aspect of machine leaming whichhas sufftcient references in established journals like ACM, IEEE etc. A surrunary of existing theorieson the exercise undertaken, alongwith exp€rience encountered by the student and the learningacquired should be presented as a seminar. The report must be free of plagiarism. A committeeconsistinS of at least three faculty members shall assess the presentation of the seminar and awardmarks to the students based on merits of topic of presentation.
Each student shall submit two copies ofa write up ofthe seminar report. one copy shall be returnedto the student after duly certifiing it by the chairman ofthe assessing committee and the other willbe kept in the departmental library. Internal continuous assessment marks are awarded based on therelevance of the experiment, presentation skill, quality of the report and participation.
objectives: To enable the stud,ent to solve problems related. to machine learning, using oppropiate tools andtechniques.
Tools :
Numerical computing Environments - GNU octave or equivalent, virtual labs (vlab.co.in) etcExperiments related to:
1. Linear Regression
2. Logistic Regression
3. Regularisation4. Principal Component Analysis5. Naive Bayes
The implementation of these algorithms must be aimed as solving problems from the domains ofsignal, speech, image, video, or natural language processing.
Interrral continuous assessment (ICA) : 1OO marksRegularity - :o marksRecord and book-keeping : 20 markTest and Viva :50 marksTotal : 1OO mark
Machine LearningHours/week :
Lecture - 3 and Tutorial - 1
This course aims at the mkli e ofseveral machine lea ning
Module I (10 h'') Learning problems - perspectives and Issues - concept Learning - version Spacesand candidate Eriminations - Inductive bias-- Decision Tree t""-rg - *"pr".entation _ Algorithm _Heuristic Space Search.
2' Ethem Arpaydin, "In*od,uaion a Machine Leaming (Adaptive computatbn Md MachineLeaming)" ,The MtT press zoo43. T. Hastie, R. Tibshirani, J.H. Friedman, ,.Ihe
Elemelta of sutistical Leuning,,, Springer; 1edition,2ool
Internal continuous assessment (ICA) : 100 marksInternal continuous assessment is in the form of periodicar tests, assignments, seminars or acombination of all whichever suits best. There wir Le a minimum of two tests per subject. The
ilfir"T*t details are to be announced to students, right at the beginning
"r trr" ,"-*il"iy trr"
End semester Examination (EsE) : 1q) marks
question patterar
Answer any 5 questions by choosing at least one question from each module'
Module IQuestionl:20marksQuestion2:20marl$
Module IIQuestion3:20markQuestion4:20mark
Module IIIQuestion5:20markQuestion6:20marks
Module IVQuestionT:20marksQuestionS:20marks
Imbalanced LearningHours/week :
Lecture - 3 and Tutorial- I
objectives: This course provides an understanding of issues rerated. to imbaranced. datoses and on barninatechniques to know how to overcome the tt^ttrtxi,ir-i'.f,oii $i"iuJ'!i"*"0 o**ro.
Module III (rs hrs) : Class Imbto svMs, svMs and class Imb rt vector Machines - Introductionprocessing methods and algori Methods for sVMs : DataActive Learning fo. I-b;l;.; d Active LearninS - Introduction,edaptive Resaripl-*J*-" ralanced Data classiffcation,for tmbalanced proUi".r.
;#"i::.:, rtrl", Ilii an Ma' "Imbatanceil Leunins : Foundations, Atson uns anit Apptications.,,
References :
i"Ei:;",i#t^:I;;::#* b Machine Leaming (Aitaptive computqtion and, Machine
2. . T. Hastie, R. Tibshirani, J.H. Friedman, ,The Elementedition,2ool ------..r&J'r!' rrrcqman' tne Ebments of statistical Leaning", springer;1
Internal continuous assessment (ICA) : 1oo marksInternal continuous assessment.is in the form o1 periodicar tests, assignments, seminars or affi:l1t;:i:,:,flJ1Il;r;:ilu. best. rhere ,,1;;;;;,_ of rwo tests per subjecr. rheteacher. rounced to students, right at the beginning J rfr" l"_"rrl. i, ,fr"
End semester Examination (ESE) : loo marks
Question pafternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marl$Question2;20marks
Module IIQuestion3:zomarksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20marks
Module IVQuestionT:zomarksQuestion8:20marks
Soft ComputingHours/week :
Lecture - 3 and Tutorial - 1
objectives: To introiluce concepu ofvadous soft computing algorithns ard,how they couldbe usedu solvereal world problems.
Module t (ra hrs): ruzzy set rheory -Introduction to Neuro- Fuzry and soft computing- Fuzry sets-Basic Definition and Terminolory- set theoretic operations- Iraember runction fo"mulution
"naParameterization- Fuzzy rules and Fuzzy reasoning- Extension principle and Fuzry Rerations- Fuzzyif+hen Rules- Fuzzy Reasoning- Fuzry rnlerence systems- t"tam<lani ruzzy rrrodils- sugeno FuzzyModels-Tsukamoto Fuzry Models- Input Space partiti oning and Ftzry Modelling.
Module III (r: hrs): Genetic Algorithm- Basic concepts- working principle- procedures of cA-flowchart of Ge- C,enetic Representations- Initialization and Selection- G€netic operators- Mutation-Cross Over- Fitness Function- Generational Cycle- Applications.
Module tV (13 hrs): EvolutionaDr computation- Evolutionary programming- Evolution strategies-classi-der systems- cenetic programming. Applications of computational tntelligence in viousargrs.
Tert Books:1. J.S.RJant, C.T. Sun and E. Mizutani , "Neuro-Fuzzy and Soft Comprting", pHI, pearsonEducation, 2004.
2. Timothy J Ross, "Fuzzy Lqic with Engineeing Applications,', Mccraw_Hill, 1997.3. Kalyanmoy Deb ,"Muki objedive optimization using Evotationary Algoritfun,,Joln Wiley
and sons, 2002.
References:
1. S. Rajasekaran and c,A.V. pai, "Ner ral Nenorl<s, Fuz4 Logic anil Genetic Ahrithns,,, pHt,2003.
Internal continuous assessment (ICA) : 1oO EarksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment detafu are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : tOO marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20markQuestion4:20mark
Module IIIquestion5:20marksQuestion6:20marlG
Module IVQuestionT:20markQuestionS:20marks
ELECTTVES . SEMESTER II
MML1o 2o4 (A)Introduction to NLp
Hours/week :Lecture - 3 and Tutorial - I
objectives: Nctural rnnguage processing (Nt p) b an area of research anil application that erqlores howamputers can be used to understanil and manipulau naural langtage text or speeclt, and. to make amputersilo useful things.
Module I (tz hrs) Issues - Motivation - Theory of Language - Features of rndian I-anguages - Issuesin Font -Coding Techniques - sorting & searching issues.
Module II (r3 hrs) rhonolory - computationar phonorogy - words and Morphemes - segmentation{ategorization and Lemmatization - word Form Recognition - varenry - Agreement - nlguhrExpressions and Automata - Morphology - Morphological issues of lndianTransliteration.
Module III (rs hrs) probabilistic Models ofpronunciation and spelling - weighted Automata - N-Grams-corpusAnalysis-smoothing-Enhopy-parts-of-speech-Taggers-Rulebased-uiddenMarkov Models.
Module w (ra hrs) Basic concepts of syrtax - parsing Techniques - Generar Grammar rules forIndian Languages - context Free Gramnar - parsing with context Free Grammars - Top DownParser-Earley Algorithm -Features and unilication - Lexicarized and probabiristic parsing-Representing Meaning- computational Representation - Meaning stnrcture oflanguage -semanticAnalysis - Lexical semantics - wordNet - pragmatics - Discourse -Resolution - Text coherence -Dialogue Conversational Agents.
Text Eooks:1. Daniel Jurafsky andJames H. Mart n, - speech and language processing- ,prentice Hall, 2ooo.2. Roland Hausser, "Fotmdations of Compatational Lingubtics., Springer_ve ag, tggg.
References:
1, James Allen, "Natwal Lurgnge understatding-,Benjamin/cummings publishing co.r995.2. Steve Young and Gerrit Bloothooft, ,,Corpus - Based.Mertods in language atd Speech
Processing", Kluwer Academic publishers, tggz.
Internal continuous assessment (ICA) : 100 marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1Oo marks
question patternAnswer any 5 questionsty choosing at least one question from each module.Module IQuestionl:20marl6Question2:20marks
Module IIQuestion3:20marksQuestion4:20marks
Module IUQuestion5:20marksQuestion6:20marlc
Module wQuestionT:20marksQuestionS:20marks
MML10 204 (B)Sparse Signal processing
Hours/week :Lecture - 3 and Tutorial - I
objectives: Thb is an advanceil course in Signal processing. tt deals with the theories and applications of therecently expanding atea of sparse signal representitions. The aim of this aurse will be to provide an overviewof the fundamenuls and some keyilictionary lmming whib oyingl ann
praaical applicattors,
Module I (rn hours) lntroduction. underdetermined linear systems and their regularization. convexregularizers' fl minimization. conversion of regularized inverse probrems to rnlar progr;.-r.solving sparse representation problem. uniqueness and uncertainty ofsorutions for spaierepresentation, Greedy pursuit argorithms. orthogonal-matching-pursuit. convex relaxationtechniques.
Module II (r: hours) Iterative-shrinkage algorithms. EM and bound-optirnization approaches. AnIRLS-based shrinkage algorithm.
spectrum sensing for cognitive radio. Single_pixel camera. Sparse MRL
Module III ( ts hours) sparsity based adaptive ffrtering methods: sparse LMs and sparse RLS.choosing versus learning: dictionary learning for sparse representation. Analysis versus synthesismodels in sparse signal modeling,
Module IV (rn hours) From theory to practice - signar and image processing apprications. Imagecompression and recognition. Image denoising using sparse representation. Image separation viaMCA, Image inpainting. Image scale-up: sparsity based super-resolution.
Text Books:7. "Sparse and. Redund.ant Representations,,, Michael Elad, Springer, 2olo.2, "Digitll Signal And Image procexing - The Sparse way', , K. p. Soman, Elsevier, 2012.
InteEtal continuous assessment (ICA) : too marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of theiemestlr by theteacher.
End semester Examination (ESE) : 1Oo marks
Question patternAnswer anv 5 ouesany 5 questions by at least one question from each module.
Module IQuestionl:20marksQuestion2:20marl$
Module IIQuestion3:20marlGQuestion4:20marks
Module IIIQuestion5:20marlcquestion6:20marks
Module IVquestionT:20markquestion8:20marks
I
Machine TranslationHours/week :
Lecture - 3 andTutorial- 1
objectives: This co urse is an introduction to the field of machine troslation (systems that tra,yjlate spezch ortext from orc humorlanguagc to wllrlther), with a focts on statbtical opprmches,
Module t (r4 hrs) word-Based tvtodels - Machine Translation by Translating words - Learning LexicalTranslation Models - Ensuring Fluent output - Hither IBM Models - word elignment.
Module II (14 hrs) Phnse-Based Models - standard Model - Learning a phrase Translation Table -Extensions to the Translation Model - Extensions to the Reordering Model - EM Training ofphrase-Based Models. Decoding - Translation process - Beam search - Future cost Esti,Irauon - otherDecoding Algorithrrs,
1. Philipp Koehn , "statistical Machine Trarclatbn',, Cambridge Universigr press
z. Yorick Wilks, "Machine Truslation: Its Scope and,LimiB-, Springer-verlag NewYork Inc,
3. Cyril Goutte, Nicola Cancedda, Marc DJrnetman, George Foster, -Izdrn@ MachineTr06lation ", MIT press.
Internal continuous assessment (ICA) : 1(x, markslnternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best, There will be a minimum of two tests per subject. theassessment details are to be announced to students, ritht at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1oO marks
question patterrAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marks
Module IIQuestion3:20marlGQuestion4:20marks
Module IIIquestion5:20marlGQuestion6:20mark
Module IVquestionT:20marksQuestionS:20marlG
MML1o 204 (C)
MMLro 204 (D)Speech and Audio Processing
Hours/week :
Lecture - 3 and Tutorial - 1credits : 4
Objectives: This co urse imparts a detailed lotowledge of modelling of speech signak, sabband coding of speecl\
9. E. zwicker and L. Fastl, "PsJchoacoustics-facts and models", Springer-Verla8., l99o
1.2
3.4.
5.
6.
7.
Internal continuous assessment (ICA) : 10O marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best, There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1OO marks
questio! patternAnswer any 5 questions by choosing at least one question from each module,
Module IQuestionl:20markQuestion2:20marks
Module IIQuestion3:20markQuestion4:20marks
Module IIIQuestion5:20markQuestion6:20mark
Module IVQuestionT:20marksQuestionE:20marlc
MML1O 205 (A)Machine Learning for Computer vision
Hours/week :
Lecture - 3 and Tutorial- 1
credits : 4
objectives: To lecrn and understotd machine learning teclmiqaes used for amputer visiotl
Module I (13 hrs): Introduction to ComPuter vision - Image Formation - Cameras - Light and color.
Module II (rr hrs): Grouping and sepentation - Active contours - Mean Shift - SPlit and Merge -
Alignment - MNSAC. Geometric vision - Camera calibration - Structure from motion - Triangulation
- Factorization - Bundle adjustment - Constrained structure and motion.
Module III (r: hrs): Recognition - Template Matching - object detection - Face recognition -
Instance recognition - Context and scene understanding. Applications.
Module tV (lg hrs): Decision tree learning - Black box methods and genetic algorithms - Bayesian
learning and course recapih ation - Machine learning applications in Computer vision.
Referenees:
1. Richard Szeliski ,"Comput* Vbiort Abontlons ail ApPlicdtions", SPringer, 2010.
2. Forsyth, Ponce, "Computer visiotx A Modem Approach", PHl, 2011.
Internal continuous assessment (IcA) : 1fi) markslnternal continuous assessment is in the form of periodical tests, assigrrments, seminars or a
combination of all whichever suits best. There will be a minimum of two tests per subject. The
assessment details are to be announced to students, right at the beginning of the semester by the
teacher.
End semester Examination (ESE) : l(x) marks
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestion 1 : 20 markQuestion2:20mark
Module IIQuestion3:20marksQuestion4:20mark
Module IIIQuestion5:20markQuestion6:20marks
Module IVquestionT:20marlGQuestionS:20marlc
MMLIO 2o5 (B)Data MiningHours/week :
Lecture - 3 and Tutorial - 1Credits : 4
objectives: Understand the data mining process including objective idmtifimtio4 model selectio4lrypothesis
formulatbry target dau colleaion, dau preprocess, model fitting, testing/verificatioLinterprentiordevaltatio4 and application-
Module r (ra hrs) Inhoduction -Data mining, Functionalities, Data preprocessing - Data cleaning -Data Integration and Transformation - Data Reduction - Data Discretization and Concept Hierarchygeneration. Data warehouse and oLAP Technolory- what Is a Data warehouse, A MultidimensionalData Model, Data Warehouse Architecture, Data Warehouse Implementation, From DataWarehousing to Data Mining, Integration ofa Data Mining System with a Data Warehouse System
Module II (re hrs) Mining frequent Patterns, Associations, and Correlations- Basic Concepts and aRoad Map, Efficient and Scalable Frequent ltemset Mining Methods, Mining Various Kinds ofAssociation Rules, From Association Mining to Correlation Analysis, Constraint-Based AssociationMining
Module uI (12 hrs) Classilication and Prediction: - Issues Regarding classification and Prediction-classification by Decision Tree Induction - Bayesian classification - Rule Based classification -Classiffcation by Backpropagation - Support Vector Machines -Associative Classification - bzyLeamers - other Classification Methods - Prediction -Accuracy and Error Measures - Evaluating theAccuracy of a Classifier or Predictor -Ensemble Methods - Model Section. WEKA data mining tool acase study.
Module ry (12 hrs) craph tvlining, Social Network Analysis, and Multtelational Data MiningMining Object, Spatial, Mulumedia, Text, and web Data Applications and Trends in Data Mining
Text Books:1 . Jiwei Han and Micheline Kambe r , Data MinW Concepts and Techniques, 2ed Edn,
Morgan Kauftnann2. Ian H. Witten Eibe frank Mark A. Hall Morgan Kaufoian\ Data Mining P:actical
Machtne Leaning Tools and. Techniques, Third EditionReferences:
3. Trevor Hastie Robert Tibshirani Jerome Friedman, Data Miaing, Infermce, andPrediction Second,Edition , Springer Series in Statistics
Internal continuous assessment (ICA) : 1O0 marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : IOO marks
Question pattenrAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marl$
Module IIQuestion3:20marlGQuestion4:zOmarks
Module IIIquestion 5:20marksQuestion6:20marks
Module tVQuestionT:20marksQuestionS:z0marl$
MML1O zos (C)optimization Methods in Machine Learning
Hours/week :
Lecture - 3 andTutorial - Icredits : 4
Objectives: This co urse introduces arange of machinelearning models and optimization nols that are used aapply these models in practice. For the students with some ML backgoand this course will introiluce whtt lies
behind the optimimtion tcr,b often used as ablockbox os well as on rnderstonling of the trade-offs ofnamerical acatraq and theorettcal and emparicol compbxity. For the stvdents with some optimiutionbackground this coarse will introduce qvariety of applicatiorc arising in machine baning ud statistics as
well as rcvel optimtmtion methods wrgeting these applicqtions.
uodule r (r: hrs) Machine learning paradigm, empirical risk ninimization, structural riskminimization, Iearnint guarantees, introduction of vc-dimension.
Module II (13 hrs) t tachine learning models: logistic regression, support vector machines, sparse
second order cone optimization, semideffnite optimization, convex composite optimization
Module rv (13 hrs) Iraethods for convex optimization: gradient descent, Newton metho4 interiorpoint methods, active set, prox methods, accelerated gradient methods, coordinate descent, cuttinSplanes, stochastic gradient.
References:
1. Sebastian Nowozin, stephen J. Wright, Suvrit Sra, "Optimization of Machine
Learning", PHI Learning Press.2. K. P, soman, R. Loganathan, v. Ajay, "Machine Learning with SvM and other Kernel
Methods", PHI Leaming Pvt Ltd.
Internal continuous assessment (ICA) : 10o marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or a
combination of all whichever suits best. There will be a minimum of two tests per subject. The
assessment details are to be announced to students, right at the beginning of the semester by the
teacher.
End semester Examination (EsE) : 1oo marks
question patternAnswer any 5 questions by choosing at least one question from each module'
Module IQuestionl:20markQuestion2:20mark
Module IIQuestion3:20markQuestion4:20marks
Module IIIquestion5:20marksQuestion6:20mark
Module rVquestionT:20marlcQuestionE:zomarks
Seminar IIHours/week :
2 hrs practical
Objectives: To impart necessary skills to the student to Present anovel idea on any topic related A machine
leaming,based on own understanding of the uork md existing literawe'
Based on understanding ofthe student on machine learning, each student is expected to design and
execute a practical experiment or demonstration involving some aspect of machine learning which
has sulficient references in established journals like ACM, IEEE etc. A summary of existing theories
on the topic undertaken, alongwith exPerience encountered by the student and the learning
acquired sirould be presented as a serninar. The seminar report must not be the reproduction of any
o.igirul p"p", o. ,nork, and it should be free from plagiarised contents. A committee consisting of at
t"i *r""" fa.utty members shall assess the presentation of the seminar and award marks to the
students based on merits oftopic of Presentation.
Each student shall submit two copies ofa write up ofthe seminar report. one copy shall be returned
to the student after duly certifying it by the chairman of the assessing committee and the other will
be kept in the departmental library. Iniernal continuous assessment marks are awarded based on the
relevince ofthe experiment, presentation skill, quality of the report and participation'
Interaal continuous assessment (IcA) : r00 marks
Presentation * Discussion : 60
Relevance + Literature : 10
Report : 20
ParticiPation : l0Total Mark : 100
MMLIo 206 (P)
Machine Learning Lab 2
Hours/week :
2 hrs Practical
objectives: To enab le the student to solve problems related. to machine learnlng, using appropriate toob and
techniqres.
Tools :
Numerical Computing Environments - GNU Octave or equivalent' virtual labs (vlab'co'in) etc
Experiments:1. SuPervised Learning Algorithms
2. SuPPort vector Machines
3. Neural Networks
4. Unsupervised Learning Algorithms
5. Semi supervised Learning Algorithms
The implementation of these algorithms must be aimed as solving problems from the domains of
signal, speech, image, video, or natural language processing'
Intenral continuous assessment (ICA) : loo marks
Regularity - 30 marks
Record and book-keePing : zo markTest and viva : 50 marks
Total : 1oo mark
'*-'4r'E.rFg
MML1O 207 (P)
ELECTTVES - SEMESTER III
Advanced Topics in Machine LearningHours/week :
Lecture - 3 and Tutorial - I
objectives: To tearn and, understarul advanced topia in Machine Learning including Graphical Moders,computational ltaning Theory, Anaryticar Leaning arur combining lruructive arur inatydcar Learnini.
Module I (rs hrs) Graphical Models: Introduction - canonical cases for conditional Independence -Example Graphical Models - d-separation - Beliefpropagation - undirected Graphs: Markov RandomFields - tearning the Structure ofa Graphical Model - Influence Diagrams.
Module II (r3 hrs) computational Learnfug Theory: Introduction - probably rearning anApproximately correct HJrpothesis - sample compledty for Finite Hypothesis spaces-- sampreComplexity for Inlinite Hypothesis Spaces - The Mistake Bound Model for Learning.
Module III (r3 hrs) Analnical Learning - Introduction - tearning with perfect Domain Theories:PRoLoG-EBG - Remarks on Explanation-Based Learning - Explanation-Based Learning of searchControl Knowledge.
naodule rv (r: hrs) combining Inductive and Analytical Learning - Motivation - Inductive-AnalyticalApproaches to Learning - using prior Knowredge to Initialize the Hypothesis - using priorKnowledge to Alter the Hypothesis - using prior Knowredge to Augment search operators - state ofthe art.
References:
1. Ethem Alpaydin, "Introiluction to Machine Leaming (Adaptive compatqtion and MachineLearning)", The :[,IT Press 2oo4Tom M. Mitchell , "Machine Leaming',, McGraw_Hill Science/Engineering/Math;I edition, 1997T. Hastie, R. Tibshirani, J, Friedman ,
,,The Elenns of Statistical Leaming,,,Springer; 1 edition, 2OO1
Internal continuous assessment (ICA) : 10O marksInternal continuous assessment is in the form of periodical tests, assignments, serninars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
3.
I
End semester Examination (ESE) : 1OO marks
question patternAnswer any 5 questions by choosing at least one question from each module.
I
Module IQuestionl:20marl6Question2:20marlc
Module IIQuestion3:20markQuestion4:20marlG
Module IIIQuestion5:20marksQuestion6:20marks
Module IVQuestionT:20marksQuestionS:20marks
Speech Processing in Mobile EnvironmentsHours/week :
Lecture - 3 and Tutorial - 1
objectives: This aurse imparts a detailed htowledge on building robust speech systems tn mobileenvironments.
tuodule r (ra hrs) vowel onset point Detection from coded and Noisy speech : speech databases forVOP detection - voP detection method for coded speech - Performance ofvop detection method inpresence ofspeech coding - VOP detection method for noisy speech - Performance ofvqp detectionmethod in the presence ofbackground noise.
Module II (t: hrs) consonant-Vowel Recognition in the presence ofcoding and Background Noise :
consonant-vowel unit databases - Two-stage cv recognition system - Impact ofaccuracy in vopdetection on cv recognition - performance of cv recognition system - Application ofcombinedtemporal and spectral processing metlrods for cV units recognised under background noise
Module III (r: hrs) spotting and Recognising of consonant-vowel units from continuous speech :
Two-sta8e aPProach for detection ofvowel onset points - performance ofspotting and recognition ofcv units in continuous speech - spotting and recognition ofcv units from coded and noisy speech
Module Iv (r: hrs) speaker tdentification and Time scale Modification using vops : speakeridentification in the presence ofcoding using vowel onset points - nonuniform time scalemodification using instants of signiffcant excitation and vowel onset points
Text Book :
1. K sreenivasa Rao and Anil Kumar vuppala, "speechprocessing {n Mobile Environmetts"Springer, Switzerland, 2Ot4
References:
10. Rabiner L.R. & Schafer R.W., "Digitalprocessing of Speechsigmsls',, prentice Hall Inc.r1. Ben cold & Nefson Morgan, " Speech and Audio Signal processing,',John Wiley & Sons, Inc.12. o'shaughnessy , D. " Speech CommunicatiotL Ht man and. Machinel'. eddison-w"sley.13. Thomas F. Quatieri , "Disctete-time speech signal processing: principles and. practice',
Prentice Hall, Signal Processing Series.14. Deller,J., J. Proakis, andJ. Hans en. "Disc.ete-Time processing of Speech Senals.,, Macmillan,15' Papamichalis P.E.,"Practical Approaches to speech coding', Texas Inskuments, prentice Hall16. Rabiner L.R. & Gold,"Theory and Applicatbrc of Digiul iignal prccessing',,
Prentice Hall oflndia17. Moore. B, "An lntroduction to Psychology ofhearing,', Academic press, London, 199718. E. Zwicker and L. Fastl, "psychoaaustics-facts and. mod.els',, Springer-verlag., t99o
Internal continuous assessment (ICA) : IOO marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1Oo marks
question patternAnswer any 5 questions by choosing at leastany 5 at one each module.
Module IQuestionl:20marksQuestion2;20marks
Module IIQuestion3:20markQuestion4:20mark
Module IIIQuestion5:20marksQuestion6:20marks
Module IVQuestionT:20marksQuestion8:20marks
MMLro 301 (C)Research Methodology
Hours/week :
Lecture - 3 and Tutorial - 1
Common with MCLIO 301Objectives; To give tlu s aen5 d sound. introduction to structured, methodologies reammended, whilecarrying out high end reseorcL
Module I - Research Methodologies (rz hrs) Introduction, Research and scientific methods,Objectives and Motivation ofResearch, criteria ofGood Research, research Approaches, Signilicanceof research, Type of Researches, Research methods versus Methodolory, Research problems,Defining a research problem, Research Design, Sampling Design
Module rI - Data Collection and Analysis (13 Hrs) Collection of Primary Data, Observation rnethod,Interview Method, collection of data through euestionnaires and schedules, secondary Data,Processing operations, statistics in research, Measures of central Tendenry, other methods of datacollection, collection ofsecondarlr data, processing operations, Types ofanalysis, statistics inresearch, Dispersion, Asymmetry, relationship, Simple regression ana\rsis, partial correlation
Module-Ill -Testing (u urs) HypothesisJ - Introduction, Testing ofHypothesis, procedure forh)ryothesis testing, Flow diagram for hypothesis testing, Measuring the power of hypothesis test,Tests ofHypothesis, Hypothesis testing of Means, proportions, correlation coemcients, chi squaretest, Phi coefticient, Hypothesis-ll - Introduction, Nonparametric, Distribution free Tests, sign tests,Fisher{rwin test, Spearman's Rank Correlation, Kendall's Coefticient of concordance
Module-IV - Report (14 Hrs) Report writing - Introduction and Significant, Interpretation -Meaning, Techniques, and Precautions,l-ayout ofresearch reports, Types of report, Mechanics andprecautions of writing a research report, computer role in research, computers and computertechnologr, computer system, Characteristics
References:
1. C.R. Kothari, Rusearch Methodologies - Uethods andTechniques, Second Edition, NewAge International
2. John W Best andJames V Kahn, Rese arch in Education,Eifth Edition, pHI, New Delhi3. Pauline V Young, Scientific Social Surveys and,Fcsearch, Third Editions, pHI New york.
Internal continuous assessment (ICA) : r(X) marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject, Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Exarnination (ESE) : 10O marks
question patternAnswer any 5 questions by choosing at least one question from each module.one
Module IQuestionl:20marksQuestion2:20marl<s
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20marks
Module WQuestionT:20marksQuestion8:20marks
MML10 3o2 (A)Reinforcement Learning
Hours/week :
Lecture - 3 and Tutorial - tCredits : 4
objectives: This aarse w l prwide a comprehensive introdaaion to reinforcement leaming, a pouerfulapproarh to baraing from interaction to achieve gools in stochastic anil incompbtely-btown invirorunants.Pctnforcement learntng has adapted key ideas from machine leaming, operatioru researclg control theory,psychology,_and newoscience to Prodace some strikingly successful applicatbns. The foctts is on algorithms furIeaming what actions to take, and. when to take therv so as to optimiz long-term performance, This mayinvolve saoificing immediate rqward. to obtuin greater rcward. in the long-term or just to obtain moreinformation about the envbonment. The course will cwer Markov decisbn procases, dynamic progranming,temporal-difference leamirq, policy gradient reinforcanent learning methods, Monte Carlo reiiforcementleaming.methods, eligibility traces, the role of funaion approximatioq hierarchical reinforcement leamingapproaches, md the integration oflearning arul planning,
trlodule I: (rohrs) The Reinforcement Learning problem; evaluative feedback, non-associativelearning, Rewards and returns, Markov Decision Processes, Value functions, optimality andapproximation
Module ll: (t:hrs) Dyumic programming: value iteration, policy iteration, asyrchronous Dp,generalized policy iteration Monte-Carlo methods: poliry evaluation, roll outs, on policy and offpolicy learning, importance sampling ,Temporal Difference learning: TD prediction, Optimality ofTD(o), SARSA, q-learning, RJearning, Games and after states
Module uI dl3hrs) Eligibility traces: n-step TD prediction, TD(lambda), forward and backward views,Q(lambda), SARSA(lambda), replacing traces and accumulating traces, Function Approximation:Value prediction, gradient descent methods, linear function approximation, ANN based functionapproximation, lazy learning, instabiligr issues, Policy Gradient methods: non-associative learning -REINFORCE algorithm, exact gradient metho&, estimating tradients, approximate policy gradientalgorithms, actor-critic methods
Module IV:(13hrs) Planning and Learning: Model based learning and planning, prioritized sweeping,Dyn4 heuristic search, trajectory sampling, E^3 algorithm, Hierarchical RL: MAXe framework,Options framework, HAM framework, airport algorithm, hierarchical poliry gradient, Case studies:Elevator dispatching, Samuel's checker player, TD-gammon, Acrobot, Helicopter piloting
Text Books:1. R. S. Sutton and A. c. Barto: " Reinforcemmt Leaning: An Introduction". Cambridge, MA: MITPress, 1998.
References:
1. Dimitri P. Bertsekas and;ohn N. Tsitsiklis, "Neuro-dynarnic progrunming,',,2. Kumpati S. Narendra and M. A. L. Thathachar, tt Learaing Automata - An Introduction'.
Internal continuous assessment (ICA) : lfi) marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or a
combination of all whichever suits best. There will be a minimum of two tests per subject. The
assessment details are to be announced to students, right at the beginning of the semester by theteacher.
End seEester ExaDination (ESE) : 1OO marks
Question pafternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20mark
Module IIQuestion3:20marl6Question4:20marlc
Module IIIQuestion5:20marksQuestion6:20marks
Module IVQuestionT:20marksQuestionS:20marks
Machine Learning for NLpHours/week :
Lecture - 3 and Tutorial - I
objectives: To leam and, anderstand machtne learning techniques usefiil for nanrallanguage processtng.
Module I (r3 hrs) Machine Learning: a brief overview-Machine Learning and Natural ranguageProcessing using Machine Learning -Data-Target function- Representing hypotheses and Jata--Choosing the learning algorithm-An example.
Module II (13 hrs) overview ofconcepts and methods -tearning and Implementing classiffcationfunctions-Text categorisation-Building a text classifier -Representing text - Dimensionality issues-classifier induction-Evaluation-other classilication tasks in NLp - practical activities
Module uI (r3 hrs) Data representation-Main algorithms-Distance and dissimilarif measures-Hierarchical clustering- K-means clustering -Applications to Natural tanguage processing- Featureextraction for text categorisation-word senses revisited -practical activities .
Module Iv (13 hrs) Hidden Markov Models,- Best path through an HMM: viterbi Decoding-unsupervised Learning of an HMM: the Baum-welch algorithm-A worked example of the firute-force) re-estimation algorithm - supervised learning and higher order models-sparsity,smoothing,Interpolation .
References:
1. European Summer School of Logic , Machine Lesming for Natural Language processing,
Language and Information ESSLU 2007, Dublin, Ireland.2'Christopher D. Manning, Hinrich 5 chuve,"Foundations of Stutistical Ndtural Luguage
Processing". MtT Press,Cambridge,1999.3. Daniel JurafslT andJames H. Marti n, " speech and r.angtnge procasing", prentice Hall, 2000.4. Roland Hausser , "Foandations of Computttional Linguisfics',, Springer-Verlag, teee.5.James A-Uen, "Naural Language rJnderstanding-,Benjamtn/cummings publishing co.1995.6. Steve Young and Gerrit Bloothooft, "Corpus - Based Methoih in Language and Speech
Processing ", Kluwer Academic publishers, t9gz.
Intemal continuous assessment (ICA) : lfi) marksInternal continuous assessment is in the form of periodical tests, assignments, seminars or acombination of all whichever suits best. There will be a minimum of two tests per subject. Theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End semester Examination (ESE) : 1OO marks
Question patternAxswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marksQuestion2:20marlG
Module IIQuestion3:20markQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20marks
Module tVquestionT:20marksQuestionS:20 marks
MMLIo 302 (c)Neural Networks for Machine Learning
Hours/week :
Lectue - 3 and Tutorial - 1credits : 4
objectives: This cowse introduces the basic moilels,barning algofinnns, od *me qplications of aewalnetworl<s fur Uachine Leuning.
Module I (tg hrs) tntroduction - The perceptron learning procedure - The Backpropagation learningprocedure - Learning feature vectors for words.
Module Ir (rr hrs) object recognition with neural nets - optimization: How to make the learning gofaster Recurrent neural networks - More recurrent neural networks.
Module III (rr hrs) ways to male neural networks general2e befter - combining multiple neuralnetworks to improve generalization - Hopfield nets and Boltzmann machines - Restricted Boltzmannmachines (R.B[,ts) - stacking RBMs to make Deep BeliefNets - Deep neural nets with generative pre-training.
Module Iv (13 hrs) Modeling hierarchical structure with neural nets - Recent applications ofdeepneural nets.
References:
1. Simon Haykins, 'Neural Netnrerks, A Comprehmsive Foundatioa,,, pearson
Education,2. B. Yegnanarayana .'Anificial Neural Netxmrk", prentice-Hall of India.3. Iaurene Fausett""Fruilomentals of Newdl Netuorl<s, Architectares, Algorittms and
Applications", P earcon.
Intemal continuous assessment (ICA) : 1O{t marksInternal continuous assessment is in the form of periodical tests, assitnments, seminars or acombination of all whichever suits best, There will be a minimum of two tests per subject. theassessment details are to be announced to students, right at the beginning of the semester by theteacher.
End senester Examination (tlSE) : l(x) marl(s
Question patternAnswer any 5 questions by choosing at least one question from each module.
Module IQuestionl:20marlcQuestion2:20mark
Module IIQuestion3:20marksQuestion4:20marks
Module IIIQuestion5:20marksQuestion6:20mark
Module IVQuestionT:20marksquestionS:20marks
Industrial TrainingHours/week :
thr
The students have to undergo an industrial training of minimum two weeks in an industry duringthe semester break after second semester and complete within 15 calendar days from the start ofthird semester, successful completion of any massive open-online course related to machinelearning from coursera, udacity, Edx or equivaren! completed during the period of the ongoing MTech programme would also be treated as equivalent to industrial trainint. 'rhe students"have tosubmit a report ofthe training or produce certificate ofcompletion of online course undergone andpresent the contents before the evaluation committee constituted by the department. ariinternalevaluation will be conducted for examining the qualif and authenticity of conients ofthe report andaward the marks at the end ofthe semester.
Internal continuous assessment (ICA) : 50 marks
Masters Research project (phase _ I)Hours/week :
Zz hr
current researclL
with the machine learning stream. The project worktudents shall be encouraged to do their project workhey may be permitted to do their project outside theclause 10 of M.tech regulations. Departrrent willconstitute an Evaluation committee to reyiew the project work. the tvaluation .o.-itt"u .on.irtofat least three faculty members of which internal guide and another expert in the specifi"J*""
"rthe project shall be two essentiar members. The student is required to undertake the mastersr and the same is continued in the ath semesterrk, two reviews of the work and the submission of
topic, objectives, methodolory and expectedrk, preliminary report and scope ofthe work
First Review 50 marks so markSecond Review 100 marks 100 mark
objectives: To improve the professional competmcy and research aptiude by towhing the areas uhich arcotherwise tot covered by theory or laboraary classes, Tlw project work aims to develop the workpractice instudena to apply theoretical and praaical tools/tecrmiques to solve reallife problems related to industry arutctrrent r*earclt
Masters Research project phase{I is a continuation of project phase-l started in the third semester.Before the end of the fourth semester, there will be two reviews, first one at middle of the fourthsemester and the ffnal one, towards the end. In the lirst review, progress of the project work done isto be assessed. In the second review, the complete assessment (quality, quantum and authenticity) ofthe thesis is to be evaluated. Both the reviews should be conducted by guide and evalgationcommittee' This would be a pre-quali&ing exercise for the students for getting approval for thesubmission of the thesis. At least one technical paper is to be prepared for possible publication injoumal or conferences. The technical paper is to be submitted along with the theiis. The finalevaluation of the project will be external evaluation.
