Comp3503Comp3503

Knowledge Discovery Knowledge Discovery and Data Miningand Data Mining

Daniel L. Silver, Ph.D.Daniel L. Silver, Ph.D.

Comp5013Comp5013

Machine Learning Machine Learning and Data Miningand Data Mining

Daniel L. Silver, Ph.D.Daniel L. Silver, Ph.D.

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OutlineOutline

Who am I?Who am I? Objectives of the courseObjectives of the course Review of the course homepageReview of the course homepage Stuff you need to have and doStuff you need to have and do

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Who am I?Who am I?

Danny Silver - BSc(Acadia), MSc, PhD (UWO)Danny Silver - BSc(Acadia), MSc, PhD (UWO) Background/Experience:Background/Experience:

– 14 years industry experience:14 years industry experience:» 2 years N.S. Government (Systems Programmer)2 years N.S. Government (Systems Programmer)» 9 years MTT – MIS (Prog.- Project Manager/Advisor)9 years MTT – MIS (Prog.- Project Manager/Advisor)» 3 years SHL System House (Tech Architect, Project Manager)3 years SHL System House (Tech Architect, Project Manager)

– 3 years Dalhousie (1996-1999)3 years Dalhousie (1996-1999)– Started at Acadia in 1999Started at Acadia in 1999– 20 years CogNova Technologies (Private Consulting)20 years CogNova Technologies (Private Consulting)

The Bad News and the Good NewsThe Bad News and the Good News

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Who are you?Who are you?NameName CourseCourse Interest Interest

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Objectives of 3503Objectives of 3503 To introduce the processes, theory and To introduce the processes, theory and

technologies of Data Analytics:technologies of Data Analytics:– collection, cleaning and consolidation of datacollection, cleaning and consolidation of data– conversion of data into informationconversion of data into information– dissemination of that informationdissemination of that information– for the generation of human knowledge. for the generation of human knowledge.

Key discussion areas:Key discussion areas:– Data/Knowledge ManagementData/Knowledge Management– Knowledge Discovery Process Knowledge Discovery Process – Data WarehousingData Warehousing– Data MiningData Mining– Data VisualizationData Visualization

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Objectives of 3503Objectives of 3503

By the end of the course you will understand:By the end of the course you will understand:– Knowledge discovery (data analytics) process and its major Knowledge discovery (data analytics) process and its major

activities, and management issuesactivities, and management issues– Differences and relationships between deductive hypothesis-Differences and relationships between deductive hypothesis-

driven discovery and inductive data-driven modelingdriven discovery and inductive data-driven modeling

– Fundamentals of data warehousing, data mining and data Fundamentals of data warehousing, data mining and data visualizationvisualization

– Fundamentals of supervised and unsupervised learningFundamentals of supervised and unsupervised learning

– Major management and technical issues surrounding data security Major management and technical issues surrounding data security and privacyand privacy

– Have hands-on experience with statistical, data mining, and data Have hands-on experience with statistical, data mining, and data visualization softwarevisualization software

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Objectives of 5013Objectives of 5013 To introduce the processes, theory and To introduce the processes, theory and

technologies of Data Analytics (KDD and DM)technologies of Data Analytics (KDD and DM) To provide fundamental theory of machine To provide fundamental theory of machine

learninglearning To provide experience at developing and testing To provide experience at developing and testing

ML software ML software Key learning areas:Key learning areas:

– Supervised learning Supervised learning – Unsupervised learningUnsupervised learning– Semi-supervised methodsSemi-supervised methods– Deep learning architecturesDeep learning architectures– Reinforcement learning (if time allows)Reinforcement learning (if time allows)

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Joint Structure of CoursesJoint Structure of Courses

There is no TAThere is no TA 1:30-3:00pm on Tues/Thur:1:30-3:00pm on Tues/Thur:

– 3503 classes3503 classes– Joint classes for common materialJoint classes for common material

4:30-6:00pm on Tues/Thurs:4:30-6:00pm on Tues/Thurs:– 5013 classes5013 classes– Joint tutorials Joint tutorials

Review 3503 course Review 3503 course homepage

Review 5013 course Review 5013 course homepage

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Stuff you will need to haveStuff you will need to have

Text books (see websites)Text books (see websites) Tech Services compliant laptop Tech Services compliant laptop Software: Software:

– MS Office or Open Office suiteMS Office or Open Office suite– Weka Data Mining environment (Mac,Win)Weka Data Mining environment (Mac,Win)– Ward Systems Group NS2 (Ward Systems Group NS2 (Windows only)Windows only)– 3503: IBM Cognos Insight (Windows only)3503: IBM Cognos Insight (Windows only)– 5013: C, Java, Matlab programming environ.5013: C, Java, Matlab programming environ.

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Stuff you will need to doStuff you will need to do

Come to classCome to class– Deeper discussion of issuesDeeper discussion of issues– HandoutsHandouts– QuizzesQuizzes

Come to class prepared Come to class prepared – Read material in advanceRead material in advance– Be prepared to answer and ask questionsBe prepared to answer and ask questions

THE ENDTHE END

danny.silver@acadiau.cadanny.silver@acadiau.ca