Lectures will initially be held on Zoom. The link will be made available on Canvas (for registered students), and will be been e-mailed to students on the wait list.

Tutorials: Mondays and Wednesdays (4-5, beginning January 17)

Links will be made available as above, and we may eventually move to DMP 101.

Instructor: Mark Schmidt.

Office hours: Fridays (beginning at 4, in ICICS 146)

**Synopsis**: This course is intended as a second or third university-level course on machine learning,
a field that focuses on using automated data analysis for tasks like pattern recognition and prediction.
The class is intended as a continuation of CPSC 340 (or 532M), and will assume a strong background in math
and computer science. Topics will (roughly) include deep learning, generative models, latent-variable models,
Markov models, probabilistic graphical models, and Bayesian methods.

**Registration**: Graduate and undergraduate students from any department are welcome to take the class. Undergraduate students should enroll in CPSC 440
while graduate students should enroll in CPSC 540. Below are more details on registration for each course:

- The majority of the seats in 440 are reserved for UBC computer science majors. For other students,
**to enroll in the course you need to sign up for the wait list**. - The seats in 540 are reserved for graduate students in CPSC, EECE, or STAT. If these seats are full or you are from a different department,
**to enroll in the course you need to sign up for the wait list**. Please read about the differences between 340/532M and 440/540 below, and**do not take this course "because 340/532M is full"**or not offered in a given term.

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.

**CPSC 340/532M vs. CPSC 440/540**:
CPSC 340 and CPSC 440 are roughly structured as one full-year course. CPSC 340 (which is occasionally listed as CPSC 532M for graduate students) covers more data mining methods
and the methods that are most widely-used in applications of machine learning
while CPSC 440 (listed as CPSC 540 for graduate students) focuses on probabilistic methods which appear in more niche applications.
It is strongly recommended that you take CPSC 340 first, as it covers the most fundamental ideas as well as the most common and practically-useful techniques.
In 440 it will be assumed that you are familiar with all the material in the current offering of CPSC 340,
and note that online machine learning courses (and courses from many other universities)
are *not* an adequate replacement for CPSC 340 (they typically have more overlap with our applied machine learning course, CPSC 330).

**Prerequisites**:

- CPSC 320: Intermediate Algorithm Design and Analysis. Note that this course itself has the following prerequisites:
- Basic algorithms and data structures (CPSC 221 or EECE 320).

- CPSC 340: Machine Learning and Data Mining. Note that this course itself has the following additional prerequisites:
- Linear algebra (one of MATH 152, 221, or 223).
- Probability (one of STAT 241, STAT 251, ECON 325, ECON 327, MATH 302, STAT 302, or MATH 318).
- Multivariate calculus (one of MATH 200, 217, 226, 253, or 263).

**Textbook**: There is no textbook for the course, but the textbook with the most extensive coverage of many of the course's topics is Kevin Murphy's Machine Learning: A Probabilistic Perspective (MLAPP, 1st Edition). This book can be purchased from Amazon,
is on reserve in the CS Reading Room (ICCS 262), and can be accessed through the library here. Optional readings will be given out of this textbook, in addition to other free online resources.

**Notes**: These are additional notes mentioned in the lectures and homeworks:

- Calculus
- Linear Algebra
- Probability
- Norms
- Max and Argmax
- Julia Commands
- Gradient and Hessian of Quadratics
- Gaussians with Conjugate Priors
- Expectation Maximization
- Forward-Backward for HMMs

**Related Courses**: Besides CPSC 340, other closely-related courses available at UBC include 500-level classes taught by Frank Wood, Leonid Sigal, Helge Rhodin, Kwang Moo Yi, Danica Sutherland, Jeff Clune, and Mijung Park. Related courses from other departments include
EECE 360/592, EOSC 510/550, STAT 305/306/406/460/461, and many 500-level STAT courses.
There is some discussion of how 340/440 relate to some of the undergraduate STAT classes written by a former student (Geoff Roeder) here, although it's getting a bit out of date (let me know if you have taken these courses recently and want to offer an update perspective).

Some related courses that have online notes are:

- Machine Learning (UBC 2013)
- Introduction to Machine Learning (Alberta)
- Machine Learning (MIT)
- Course in Machine Learning (Maryland)
- Apprentissage (ENS, in French)

- Machine Learning (Mathematical Monk)

Mark Schmidt > Courses > CPSC 440