Winter semester 2019/2020.

A pdf version containing all this info is Courseinfo.pdf

Lecturers: Isabel Valera and Caterina De Bacco

For any general question about the course, use GitHub issues. Before posting, please make sure your question has not been previously answered. Only in case of private question, send us an email.

Plan: 14 Oct 2019 - 9 Feb 2020, 15 weeks, 4hr/week, 15 weeks, 60hr.

Lectures: [NEWS] Tuesdays 14:15-16pm at TTR2 in Cyber Valley Campus.

Tutorials: Wednesdays 16:15-18pm at TTR2 in Cyber Valley Campus.

Registration: Register (informally) to the course if you want to receive general course and assignment information via: https://forms.gle/eqqijGzksdtdbtus8

For the EXAM, NEED to officially register either via Campus / ALMA or written if the student cannot register online (closer to the exam date).

Grading : 70% written exam + 30% assignments.

• 1st assignment* (corresponding to Block I) due to December 13th.

• 2nd assignment* (corresponding to Block II) due to January 17th.

• 3rd assignment* (corresponding to Block III) due to February (date to be fixed).

*Every assignment is composed by several exercises, which will be released sequentially before every tutorial session. Information about assignment submission will be provided later in time but It will be made electronically.

*Assignment may be done and submitted in groups of up to 3 people (optional).

1. Introduction to probabilistic machine learning (15 Oct )
   • Reference: Chapter 2 up to Section 2.3.6 and Section 8.2 of Bishop

2. Gaussian Mixture Model (GMM) + Expectation Maximization (22 Oct)
   • Reference: Section 9.2 of Bishop
3. DP- GMM + Gibbs Sampling (29 Oct)
4. Hidden Markov Models (HMMs) + Gibbs (5 Nov)
5. Temporal point Processes (TPPs) I (12 Nov)
6. TPPs + Sequential Monte Carlo (19 Nov)

7. Mean Field approach (26 Nov)
8. TAP (3 Dec)
9. Review and Spin glass planted I (10 Dec)
10. Spin glass planted (BP) (17 Dec)
11. MM-SBM + EM/BP (7 Jan)

12. GMMs + Variational Inference (VI) (14 Jan)
13. VI + LDA (21 Jan)
14. Stochastic VI (28 Jan)
15. Variational Autoencoders (4 Feb)

• Bishop=C. M. Bishop, Pattern recognition and machine learning (Springer, 2006).