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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 4-May-17 Daniel L. Silver 3 Who am I? 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) The Bad News and the Good News 4-May-17 Daniel L. Silver 4 Who are you? Name 4-May-17 Course Interest Daniel L. Silver 5 Objectives of 3503 To introduce the processes, theory and technologies of Data Analytics: – – – – collection, cleaning and consolidation of data conversion of data into information dissemination of that information for the generation of human knowledge. Key discussion areas: – – – – – 4-May-17 Data/Knowledge Management Knowledge Discovery Process Data Warehousing Data Mining Data Visualization Daniel L. Silver 6 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 4-May-17 Daniel L. Silver 7 Objectives of 5013 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: – – – – – 4-May-17 Supervised learning Unsupervised learning Semi-supervised methods Deep learning architectures Reinforcement learning (if time allows) Daniel L. Silver 8 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 4-May-17 on Tues/Thurs: Daniel L. Silver 9 Review 3503 course homepage Review 5013 course homepage Stuff you will need to have Text books (see websites) Tech Services compliant laptop Software: – – – – – 4-May-17 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 12 Stuff you will need to do Come to class – Deeper discussion of issues – Handouts – Quizzes Come to class prepared – Read material in advance – Be prepared to answer and ask questions 4-May-17 Daniel L. Silver 13 THE END [email protected]