Instructor: Mark Schmidt
Instructor office hours: TBA
Tutorials (beginning September 11):
Teaching Assistants: TBA
TA office hours: TBA
Synopsis: We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the `big data' buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We'll focus on many of the core data mining and machine learning technlogies, with motivating applications from a variety of disciplines.
Registration: Undergraduate and graduate students from any department are welcome to take the class. However, due to the high demand only UBC computer science majors can directly register for the course. For all other students, to enroll in the course you need to sign up for the wait list. Note that last year all students on the wait list were ultimately accepted into the course (but we did not have room for auditors.)
Textbook: There is no required textbook for the class. A introductory book that covers many (but not all) the topics we will discuss is the Artificial Intelligence book of Rusell and Norvig (AI:AMA) or the Artificial Intelligence book of Poole and Mackworth (you may need these for other classes). More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy's Machine Learning: A Probabilistic Perspective (ML:APP) which can be accessed through the library here, and Bishop's Pattern Recognition and Machine Learning (PRML). For books with a bigger focus on data mining, see Introduction to Data Mining (IDM) and Mining of Massive DataSets.
Related Courses: Related courses in statistics include: STAT 305, STAT 306, STAT 406, STAT 460, STAT 461 (as well as EOSC 510). A discussion of the difference between CPSC 340 and these various STAT classes written by a former student (Geoff Roeder) is available here.
Grading: Assignments 30%, Midterm 30%, Final 40%.
Piazza for course-related questions.
|Date||Topic||Related Readings and Links||Homework and Notes|
|Wed Sep 6||Syllabus||Machine Learning Rise of the Machines Talking Machine Episode 1|