CPSC 340 and 532M - Machine Learning and Data Mining (Fall 2019)

Lectures Sections (beginning September 4): Instructor: Mark Schmidt
Instructor office hours: TBA

Tutorials (beginning September 9):

Teaching assistants: TBA
TA office hours (beginning September 9, all in Demco Learning Centre): 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 course. Undergraduate students should enroll in CPSC 340 while graduate students should enroll in CPSC 532M (which has an extra small project component). Below are more details on registration for each course:

Starting in the second week of classes, we'll have weekly tutorials run by the TAs. These will do things like go through provided assignment code, review background material, review big concepts, and/or do exercises. You can register for particular tutorial sections if you want to save a seat at a particular time, but note that you do not need to register in a tutorial section.

Prerequisites:

***Students who completed STAT 200, STAT 203, PSYC 218, PSYC 278, BIOL 300 or COMM 291 with a grade of at least 72%, prior to 2019W1 will be allowed to take CPSC 340 in 2019W, provided they have all of the other stated prerequisites. Students who do not meet these requirements should consider taking CPSC 330, a new course on applied machine learning.

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: The most related course is CPSC 330: Applied Machine Learning. This course has fewer prerequisities and covers some of the same material, but focuses more on applications rather than understanding ML ideas in depth.

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:

List of topics

We will roughly cover the following topics:

Lectures, Assignments, Related Readings, and Links

Coming soon.

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