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EECS738 Xue-wen Chen
EECS 738: Machine Learning
Fall 2011, Prof. Xue-wen ChenThe University of Kansas
EECS738 Xue-wen Chen
Machine Learning
• Predict the unknown from uncertain information
EECS738 Xue-wen Chen
Why Machine Learning?
EECS738 Xue-wen Chen
Speech Recognition
Hidden Markov models and their generalizations
EECS738 Xue-wen Chen
Tracking and Robot Localization
[Fox et al.] [Funiak et al.]
Kalman Filters
EECS738 Xue-wen Chen
Evolutionary Biology
[Friedman et al.]
Bayesian networks, Sequence alignment …
EECS738 Xue-wen Chen
Modeling Sensor DataUndirected graphical models
[Guestrin et al.]
EECS738 Xue-wen Chen
Planning Under Uncertainty
F’
E’
G’
P’ Peasant
Footman
Enemy
Gold
R
t t+1TimeAPeasant
ABuild
AFootman
P(F’|F,G,AB,AF)
[Guestrin et al.]
Dynamic Bayesian networksFactored Markov decision problems
EECS738 Xue-wen Chen
Images and Text Data
[Barnard et al.]
Hierarchical Bayesian models
EECS738 Xue-wen Chen
Structured Data (text, webpage,…)
[Koller et al.]Probabilistic relational models
EECS738 Xue-wen Chen
And many
many
many
many
manymore…
EECS738 Xue-wen Chen
Syllabus• About me the course (see the syllabus)• Covers a wide range of machine learning
topics (if time permits): from basic to state-of-the-art– Fundamentals– Supervised and unsupervised– SVM, NN, DTs– Bayesian networks– MCMC, Gibbs, EM– Gaussian and hybrid models, discrete and continuous variables– temporal and template models, hidden Markov Models, – Forwards-Backwards, Viterbi, Baum-Welch, Kalman filter,
• Covers algorithms, theory and application
EECS738 Xue-wen Chen
Prerequisites• Mathematical maturity:
– Vector/Matrix– Probabilities: distributions, densities, marginalization…– Basic statistics: moments, typical distributions, regression…– Optimization– Ability to deal with “abstract mathematical concepts”
• Programming– Experienced in at least one language (C, C++, Java, R, Matlab …)
• It’s going to be fun and hard work– Think before you decide: for credit only or for learning something– The class will be fast paced– Willing to spending time and efforts (in classroom and out…)– Dealing with mathematical formulas … CANNOT emphasize it
more– Understand it, program it– Fun only if you enjoy it …
EECS738 Xue-wen Chen
Text Books– Machine Learning: an algorithm perspective, Stephen
Marsland, CRC Press.– (optional) Tom Mitchell. Machine Learning, 1997,
WCB/McGraw-Hill.– (optional) Pattern Recognition and Machine Learning,
Christopher Bishop, Springer– Additional handouts will be provided as needed.
EECS738 Xue-wen Chen
Grades
• Exam: 40%• Final Project: 60%• Participation 10%
• The cutoffs for grades will be roughly as follows:
A: 90 – 100 B: 80 – 89 C: 70 – 79 D: 60 – 69 F: 0 – 59
EECS738 Xue-wen Chen
Exam
• To test – if you are ready!! – If you will survive
• Include but not limited to– Linear algebra– Matrix calculus– Probability ad Statistics– Optimization
EECS738 Xue-wen Chen
Project• Choose a topic that is related to your research interest and
pertains to the course material.
• The proposal should include the following sessions (Due: October 24) – the project goal, – the problems to be studied, – overview of current methods, – proposed methods, – expected results, and – references (about 4 pages: single space, fond size = 12, references
are not counted). References should be cited in the proposal.
• A written final report in the style of a journal article is also required. Final project is due by Dec. 07 (no late written project).
• Each student will give classroom presentation about the final project.
• Details: see the syllabus
EECS738 Xue-wen Chen
Some Important Dates
• October 24 – Project Proposal Due
• December 07 – Final project (written) due
EECS738 Xue-wen Chen
Tentative Lectures
• See syllabus• Preliminaries:
– matrix, statistics, optimization • Questions?