This course introduces probabilistic learning tools such as exponential families, directed graphical models, Markov random fields, exact inference techniques, message passing, sampling and mcmc, hidden Markov models, variational inference, EM algorithm, Bayesian regression, probabilistic PCA, Neural networks kernel methods, Gaussian processes, variational autoencoders, and diffusion models. It will also offer a broad view of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses.

More details can be found in syllabus and piazza.

- Final exam is in-person on 18 Apr 7pm-10pm in EX 310 (A-DE) and EX 320 (DEN-Z). Practice final exam and further info is here.
- No tutorials on 3/29 since the University is closed.
- A3 deadline is extended to 3/31 23:59.
- A3 is out, and due on 3/26 23:59 (start early). TA office hours are 3/21 10-11, 3/22 15-16 at Pratt 286.
- You may submit A2 by Feb 22, without any penalty.
- A representative practice midterm is released along with its solutions.
- Midterm logistics can be found here. A1 solutions are posted on quercus.
- A2 is out, and due on 02/18 23:59. TA office hours are 02/15 15-16, 02/16 17-18 at BA2270.
- A1 is out, and due on 02/04 23:59. TA office hours are 01/30 12-13, 02/01 13-14 at BA2270.
- Lectures begin on Jan 8!

Prof | Murat A. Erdogdu |
---|---|

csc412prof@cs.toronto.edu | |

Office hours | W 15-17 @Pratt 286b |

Vahid Balazadeh, Daniel Eftekhari, Alireza Keshavarzian, Alireza Mousavi (Head TA), Mert Vural, Haoping Xu, Matthew Zhang

- Email: csc412ta@cs.toronto.edu

Section | Lecture | Tutorial |
---|---|---|

CSC412 LEC0101-2001 & CSC2506 LEC0101 | M 13-15 @ GB 119 | F 13-14 @ GB 119 |

CSC412 LEC0201 & CSC2506 LEC0201 | W 13-15 @ MP 137 | F 14-15 @ ES B142 |

No required textbooks. Suggested reading will be posted after each lecture (See lectures below).

- (PRML) Christopher M. Bishop (2006) Pattern Recognition and Machine Learning
- (MLPP) Kevin P. Murphy (2012), Machine Learning: A Probabilistic Perspective
- (PML1) Kevin P. Murphy (2022), Probabilistic Machine Learning: An Introduction
- (PML2) Kevin P. Murphy (2023), Probabilistic Machine Learning: Advanced topics
- (ITIL) David MacKay (2003) Information Theory, Inference, and Learning Algorithms
- (ESL) Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning

Assignment # | Out | Due | TA Office Hours |
---|---|---|---|

Assignment 1 | 1/22 | 2/04 | 1/30 12-13, 2/01 13-14 at BA2270 |

Assignment 2 | 2/05 | 2/18 | 2/15 15-16, 2/16 17-18 at BA2270 |

Assignment 3 | 3/05 | 3/26 | 3/21 10-11, 3/22 15-16 at Pratt286 |

For the assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn. You have two options:

- The easiest option is run everything on colab.
- Alternatively, you can install everything yourself on your own machine.
- If you don’t already have python, install using Anaconda.
- Use pip to install the required packages
`pip install scipy numpy autograd matplotlib jupyter sklearn`

- For those unfamiliar with Numpy, there are many good resources, e.g. Numpy tutorial and Numpy Quickstart.