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, and variational autoencoders. 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 24 Apr 9am-12pm at BN 322. Practice final exam and further info is here.
- Hw 3 is out, and due on 03/27 23:59. TA office hours on 03/22 & 03/23, 1-2pm at UY9040.
- Midterm logistics can be found here. Hw1 solutions are posted on quercus.
- OHs will be online during the reading week. Zoom link.
- A representative practice midterm is released with solutions.
- Hw 2 deadline extended to Feb 22, 23:59.
- Hw 2 is out, and due on 02/19 23:59. TA office hours 2/14 & 2/15 14-15 online link.
- Hw 1 is out, and due on 02/05 23:59. TA office hours are on 1/30 & 2/01, 12:30-13:30 at Hydro 9013.
- Instructor OHs are online this week (01/17).
- Lectures begin on Jan 9!

Prof | Murat A. Erdogdu & Piotr Zwiernik |
---|---|

sta414-2104prof@cs.toronto.edu | |

Office hours | T 15:30 -17:30 (UY 9040) |

Hossein Yousefi, Alireza Mousavi, Daniel Eftekhari, Madhu Gunasingam

- Email: sta414-2104ta@cs.toronto.edu

Section | Room | Lecture time | Zoom link |
---|---|---|---|

STA 414 LEC0101 & STA 2104 LEC0101 | MS 2172 | M 14-17 | link |

STA 414 LEC5101 & STA 2104 LEC5101 | MS 2172 | T 18-21 | link |

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

For the homework 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.