CPSC 340 and 532M - Machine Learning and Data Mining (Fall 2018)

Lectures (beginning September 5): Mondays, Wednesdays, and Fridays from 4-5 (MacMillan 166).

Instructor: Mark Schmidt.
Instructor office hours: TBA.
Tutorials begin September 10.
Teaching Assistants: TBA
TA office hours (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:

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.

Grading:

Piazza for course-related questions.

List of topics

We will roughly cover the following topics:

Timetable

Date Slides Related Readings and Links Homework and Notes
Wed Sep 5 Motivation and Syllabus What is Machine Learning? Machine Learning
Rise of the Machines Talking Machine Episode 1

Related Courses

Related courses in statistics at UBC 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. Related courses from other universities that have online notes include:

Mark Schmidt > Courses > CPSC 340