CMPT 726: Machine Learning, Fall 2019Instructor: Greg Mori
Lectures: Monday 16:30-19:20 in B9200
TAs: Akash Abdu Jyothi <email@example.com>, Lei Chen <firstname.lastname@example.org>, Ruizhi Deng <email@example.com>, Sha Hu <firstname.lastname@example.org>, Mengyao Zhai <email@example.com>
Office hours: Calendar
- Block 1: Grad students, CMPT MSc/PhD thesis, other
- Block 2: Grad students, CMPT Prof. MSc (last name A-L)
- Block 3: Grad students, CMPT Prof. MSc (last name M-Z)
About the course
Machine Learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting aspects of artificial intelligence, and is the basis for many of its industrial applications. It is the preferred framework for many applications, such as face detection, hand-written digit recognition, speech recognition, and credit card fraud detection.
This course is a 700-level grad course - it will be a lecture-style course and be taught from a textbook. This course will start from the basics, no prior experience in machine learning nor pattern recognition will be presumed. Students will gain hands-on experience with state of the art machine learning algorithms via programming assignments on real datasets and a course project.
The most important prerequisite for this course is a strong mathematics background. It will be possible to refresh your knowledge at the beginning of the course, but I don't want anyone to run from the room screaming if I say "eigenvector" or "covariance matrix."
Assignment submission and grading:
- Journal of Machine Learning Research (JMLR)
- Neural Information Processing Systems (NIPS)
- International Conference on Machine Learning (ICML)
- Uncertainty in Artificial Intelligence (UAI)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)