The course will introduce predictive modelling techniques as well as related statistical and visualization tools for data mining. The course will cover common machine learning techniques that are focused on predictive outcomes. Students will learn how to evaluate the performance of the prediction models and how to improve them through time.
Saturday, May 10 - August 9: 9:00 a.m. - 12:00 p.m. (Virtual)
Location:
ONLINE
Instructor:
Haitham Amar
Notes:
This course is offered as a virtual classroom with classes held, as per the scheduled day/time. Classes will be held online, using a combination of synchronous, real-time, instructor led sessions, supplemented with asynchronous activities. You should expect to attend class at the same time, each week, although not every session will be a full 3 hours of live instruction. Rather, classes will provide learners the opportunity to apply what has been covered by your course facilitator. Laptop computer required; No class May 17, and August 2. Please note registering after the course start date may delay course access and does not permit exceptions to coursework due dates, late textbook purchases, or the refund policy
Instructors
Haitham Amar
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Haitham Amar currently works as a Lead Data Scientist in the field of online commerce and SaaS. He is also a sessional instructor at University of Waterloo teaching various graduate level AI courses. He has been working in the field of data analytics and AI since 2010. He is a member of the Centre of Pattern Analysis and Machine Intelligence (CPAMI) at the University of Waterloo. He received his Ph.D. degree in the field of communication and information systems from University of Waterloo. He also received his MASc. degree in Electrical and Computer Engineering from the University of Waterloo, and his B.Sc. degree in Computer Engineering from University of Tripoli.
His current research interests revolve around market analysis, recommendation systems, machine learning, artificial intelligence, pattern recognition, situation assessment and decision aid systems, vehicular localization, cognitive radios, trip planning and applied game theory.
Karim is Statistician by training, a Certified Analytics Professional, an active member of the American Statistical Association, and a DASCA certified Senior Data Scientist. He’s also a former Fulbright scholar at U.C. Berkeley. His past teaching experience includes Big Data Analytics courses and Capstone project supervision at Ryerson University and Sheridan College. Karim has also held multiple senior analytics positions in the public service of Canada and Ontario. He’s most passionate about non-linear dimension reduction methods.
