Welcome to the class! The course Google Calendar can be found here.
The course mailing list is cse416-staff [at] cs.washington.edu. Please USE THE DISCUSSION BOARD for non-personal issues. If you do have a personal issue, please use this list instead of emailing TA's directly. We'll be able to all see it and therefore respond faster.
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 methodologies, technologies, and algorithms of machine learning. The topics of the course draw from classical statistics, from machine learning, from data mining, 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.
- Lecture: Tuesday/Thursday 10-11:20am, MLR 301
- TA Office Hours: See people for locations.
- Emily's Office Hours: Thursday, 11:30am - 12:30pm in CSE 568
- Quiz Sections
- AA (Varun): Thursday 12:30 - 1:20, DEM 012
- AB (Hunter): Thursday 1:30 - 2:20, THO 101
- AC (John/Patrick): Thursday 2:30 - 3:20, SAV 139
- AD (Devin): Thursday 3:30 - 4:20, SAV 137
- Assignments (60%)
- Concept Quizzes (15%)
- Final Exam (25%)
Textbooks (all optional)
- A Course in Machine Learning; Hal Duame
- Machine Learning: A Probabilistic Perspective; Kevin Murphy
- Pattern Recognition and Machine Learning; Chris Bishop
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani, Jerome Friedman