Download Introduction and Overview

Comp3503
Knowledge Discovery
and Data Mining
Daniel L. Silver, Ph.D.
Comp5013
Machine Learning
and Data Mining
Daniel L. Silver, Ph.D.
Outline
Who am I?
 Objectives of the course
 Review of the course homepage
 Stuff you need to have and do
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4-May-17
Daniel L. Silver
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Who am I?
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Danny Silver - BSc(Acadia), MSc, PhD (UWO)
Background/Experience:
– 14 years industry experience:
» 2 years N.S. Government (Systems Programmer)
» 9 years MTT – MIS (Prog.- Project Manager/Advisor)
» 3 years SHL System House (Tech Architect, Project Manager)
– 3 years Dalhousie (1996-1999)
– Started at Acadia in 1999
– 20 years CogNova Technologies (Private Consulting)
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The Bad News and the Good News
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Daniel L. Silver
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Who are you?
Name
4-May-17
Course
Interest
Daniel L. Silver
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Objectives of 3503

To introduce the processes, theory and
technologies of Data Analytics:
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collection, cleaning and consolidation of data
conversion of data into information
dissemination of that information
for the generation of human knowledge.
Key discussion areas:
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Data/Knowledge Management
Knowledge Discovery Process
Data Warehousing
Data Mining
Data Visualization
Daniel L. Silver
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Objectives of 3503

By the end of the course you will understand:
– Knowledge discovery (data analytics) process and its major
activities, and management issues
– Differences and relationships between deductive hypothesis-driven
discovery and inductive data-driven modeling
– Fundamentals of data warehousing, data mining and data
visualization
– Fundamentals of supervised and unsupervised learning
– Major management and technical issues surrounding data security
and privacy
– Have hands-on experience with statistical, data mining, and data
visualization software
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Daniel L. Silver
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Objectives of 5013
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To introduce the processes, theory and
technologies of Data Analytics (KDD and DM)
To provide fundamental theory of machine
learning
To provide experience at developing and testing
ML software
Key learning areas:
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Supervised learning
Unsupervised learning
Semi-supervised methods
Deep learning architectures
Reinforcement learning (if time allows)
Daniel L. Silver
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Joint Structure of Courses
 There
is no TA
 1:30-3:00pm on Tues/Thur:
– 3503 classes
– Joint classes for common material
 4:30-6:00pm
– 5013 classes
– Joint tutorials
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on Tues/Thurs:
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Review 3503 course
homepage
Review 5013 course
homepage
Stuff you will need to have
Text books (see websites)
 Tech Services compliant laptop
 Software:
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MS Office or Open Office suite
Weka Data Mining environment (Mac,Win)
Ward Systems Group NS2 (Windows only)
3503: IBM Cognos Insight (Windows only)
5013: C, Java, Matlab programming environ.
Daniel L. Silver
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Stuff you will need to do
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Come to class
– Deeper discussion of issues
– Handouts
– Quizzes
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Come to class prepared
– Read material in advance
– Be prepared to answer and ask questions
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THE END
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