Previous offerings by Mark Schmidt: 2021w2, 2020w2. This instance will be broadly similar, but not the same.
Italicized entries are tentative; in particular, the timing and even number of assignments might change. Textbook acronyms are explained below.
Date | Topic/slides | Supplements | |
---|---|---|---|
M | Jan 9 | Syllabus Binary density estimation | ML vs. Stats, 3 Cultures of ML Math for ML, Essence of Linear Algebra PML1 2.1-2.4 / MLAPP 2.1-2.3 |
Tu | Jan 10 | Assignment 1 released: pdf, tex, zip | |
W | Jan 11 | MAP | MLAPP 3.1-3.3 / PML1 4.5, 4.6.2 |
M | Jan 16 | Generative vs discriminative classifiers; neural nets | MLAPP 3.5, 8.1-8.3, 8.6, 16.5 PML1 9.3, 10.2, 13.2 |
W | Jan 18 | Double descent; deep networks; autodiff | PML1 13.1-13.3 Double descent papers: 1 2 3 |
F | Jan 20 | Add/drop deadline | |
M | Jan 23 | Assignment 1 due at noon (extended) | |
M | Jan 23 | Convolutions, auto-encoders, multi-label classifiers | PML1 14.1-14.3, 20.3 MLAPP 28.3 |
W | Jan 25 | Fully-convolutional nets, categorical variables, Monte Carlo | PML1 14.4.2, 14.5.4; 2.5; PML2 11.2 MLAPP 2.7, 23.1 |
M | Jan 30 | Bayesian learning | |
T | Jan 31 | Assignment 2 released: pdf, tex, zip | |
W | Feb 1 | More Bayesian learning; Empirical Bayes | |
M | Feb 6 | Hierarchical Bayes; multiclass classification | |
W | Feb 8 | ||
M | Feb 13 | ||
W | Feb 15 | ||
F | Feb 17 | Assignment 2 due at 11:59pm | |
M | Feb 20 | Class cancelled: Family Day + midterm break | |
W | Feb 22 | Class cancelled: midterm break | |
M | Feb 27 | ||
M | Feb 27 | Assignment 3 released | |
W | Mar 1 | ||
F | Mar 3 | Withdrawal deadline | |
M | Mar 6 | ||
W | Mar 8 | ||
M | Mar 13 | ||
W | Mar 15 | ||
F | Mar 17 | Assignment 3 due at 11:59pm | |
M | Mar 20 | ||
M | Mar 20 | Assignment 4 released | |
W | Mar 22 | ||
M | Mar 27 | ||
W | Mar 29 | ||
M | Apr 3 | ||
W | Apr 5 | ||
Th | Apr 6 | Assignment 4 due at 11:59pm | |
M | Apr 10 | Class cancelled: Easter Monday | |
W | Apr 12 | ||
? | Apr ? | Final exam (in person, handwritten); sometime between 17th and 28th, inclusive | |
F | Apr 28 | Final project due at noon | |
F | Apr 28 | Lecture+assignment project due at noon |
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.
The course meets in person in Swing 122. I plan to release recordings, but can't guarantee their quality, so please come to class.
Grading scheme:
Further details in the syllabus slides.
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).
Undergraduate students will not be able to take the class without these prerequisites. Graduate students may be asked to show how they satisfy prerequisites.
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 Probabilistic Machine Learning series. The one-volume 2012 version is Machine Learning: A Probabilistic Perspective (MLAPP; you can access a PDF through the UBC library, read a hardcopy version in the CS Reading Room [ICCS 262], or buy a hardcopy). Alternately, he has a very recent two-volume version (2022/2023), PML1 and PML2, both of which have free Creative Commons draft pdfs through those links. I'll try to refer to the relevant sections of both versions as we go, as well as links to various other free online resources.
If you need to refresh your linear algebra or other areas of math, check out Mathematics for Machine Learning (Marc Deisenroth, Aldo Faisal, Cheng Soon Ong; 2020).
Related courses: Besides CPSC340, there are several 500-level graduate courses in CPSC and STAT that are relevant: check out the graduate courses taught by people on the ML@UBC page and the MILD list. CPSC 422/425/436N, EECE 360/592, EOSC 510/550, and STAT 305/306/406/460/461 are also all relevant.
Some related courses that have online notes are: