Welcome to the class!
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.
- Lecture: M/W/F 9:30-10:20am, THO 101
- TA Office Hours: 2:00-3:00pm Monday-Thursday. See people for locations.
- Additional office hours: 5:00pm-?? Thursday, the day before problem sets are due. CSE second floor breakout area.
- Emily's Office Hours: 10:30-11:30am on Fridays
- Optional tutorials Material covered:
- Jan 5: Python
- Jan 12: Linear Algebra
- Feb 2: Midterm review
- Mar 9: Final review
- 8:30-9:20am, MEB 242.
- 9:30-10:20am, MEB 242.
- 12:30-1:20pm, GLD 435. **This time not offered for Jan 5 Python tutorial.
- Midterm (15%)
- Homeworks (4 assignments 40%)
- Final project (20%)
- Final exam (25%)
- 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.