Class lectures: TTh 10:30-11:50am, EEB 045 (Campus Map)
Recitations: Wednesdays, 5:00-6:20 pm, EEB 045
It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to provide a thorough grounding in the fundamental methodologies, technologies, mathematics and algorithms of machine learning. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics, and from statistical algorithmics.
Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.Prerequisites: STAT 341, STAT 391, or equivalent, or permission of instructor.
Discussion forumIMPORTANT: All class announcements will be broadcasted using the Catalyst discussion board. The same applies to questions about homeworks, projects and lectures. If you have a question of personal matters, please email the instructors list:
Otherwise, please send all questions to this board, since other students may have the same questions, and we need to be fair in terms of how we interact with everyone. Also, please feel free to participate, answer each others' questions, etc.
- Required Textbook: Machine Learning: a Probabilistic Perspective , Kevin Murphy.
- Optional Textbook: Pattern Recognition and Machine Learning , Chris Bishop.
- Optional Textbook: The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman. 2nd edition.
- Optional textbook: Machine Learning , Tom Mitchell.
- Optional textbook: Information Theory, Inference, and Learning Algorithms , David Mackay.
- Midterm (15%)
- Homeworks (4 assignments 35%)
- Final project (30%)
- Final exam (20%)
Homework policyImportant Note: As we often reuse problem set questions from previous years, covered by papers and webpages, we expect the students not to copy, refer to, or look at the solutions in preparing their answers. We expect students to want to learn and not google for answers. The purpose of problem sets in this class is to help you think about the material, not just give us the right answers. Therefore, please restrict attention to the books mentioned on the webpage when solving problems on the problem set. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution. Reading unauthorized material will be considered cheating.
Collaboration policyHomeworks will be done individually: each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. You also must indicate on each homework with whom you collaborated.
Late homework policy
- Homeworks are due at the begining of class, unless otherwise specified, through Catalyst.
- Any assignment turned in late, will incur a reduction of 33% in the final score, for each day (or part thereof) it is late. For example, if an assignment is up to 24 hours late, it incurs a penalty of 33%. Else if it is up to 48 hours late, it incurs a penalty of 66%. And if it is 36 or more hours late, it will receive no credit.
- You are allowed to use 3 LATE DAYs throughout the entire quater only for the homeworks. Please use these wisely, and plan ahead for conferences, travel, deadlines, etc.
- You must turn in all 4 homeworks, even if for zero credit, in order to pass the course. (Empty homeworks don't count... :))