Lectures
The course comprises 10 lectures for 1RT705 and additional 4 lectures for 1RT003.
| Lecture | Lecturer | Reading | Slides |
Recording and discussion forum |
Recording from last year |
|
| 1. | Introduction, probabilistic modelling | NW | [B] 1.1-1.2.3, 8.2-8.2.1 (one page, not including 8.2.1) | pdf Download pdf | Lecture 1 | L1 -2020 |
| 2. | Conjugate prior, Binomial-Beta conjugate pair, Multivariate Gaussian distribution | NW | [B] 2.1, 2.3.1-2.3.6, 2.4, [LWLS] 9.A, Formula sheet for the Gaussian distribution Download Formula sheet for the Gaussian distribution | pdf Download pdf | Lecture 2 | Last part of L1 above and parts of L2 below |
| 3. | Bayesian linear regression, marginal likelihood | NW | [B] 3.3-3.4, [LWLS] 9.1-9.2 | pdf Download pdf | Lecture 3 | L2 -2020 |
| 4. | Gaussian processes I (only 1RT003) | AR | [LWLS] 9.3, [B] 6.4.1, 6.4.2 | pdf Download pdf | Lecture 4 | L7 -2020 |
| 5. | Gaussian processes II (only 1RT003) | AR | pdf Download pdf | Lecture 5 | L8 -2020 | |
| 6. | Bayesian graphical models | AR | [B] 8, 8.1, 8.2 | pdf Download pdf | Lecture 6 | L3 -2020 |
| 7. | Monte Carlo methods | JS | [B] 11, 11.1.4, 11.2-11.3 | pdf Download pdf | Lecture 7 | L4 -2020 |
| 8. | Markov Random Fields, Factor Graphs | NW | [B] 8.3 - 8.3.3, 8.4.3 | pdf Download pdf | Lecture 8 | L5 -2020 |
| 9. | Message passing | NW | [B] 8.4-8.4.4, 10.7.2* | pdf Download pdf | Lecture 9 | Last part of L5 above and factor graph example in L6 below. |
| 10. | Variational inference (only 1RT003) | JS | [B] 1.6.1, 10-10.1.1, 10.3-10.3.1 | pdf Download pdf | Lecture 10 | L6 -2020 |
| 11. | Unsupervised learning | TS | [B] 2.5, 12.1, 12.2 (but not 12.2.1-4) |
pdf Download pdf | Lecture 11 | L9 -2020 |
| 12. | Semi-supervised learning and generative models | TS | [LWLS] 10.1, 10.3 | pdf Download pdf | Lecture 12 | New this year |
| 13. | Variational autoencoder (only part of 1RT003) | TS | [KW] 1-2.4 | pdf Download pdf | Lecture 13 | L10 -2020 |
| 14. | Summary / guest lecture with Agrin Hilmkil, Storytel | NW | pdf Download pdf | Lecture 14 |
(you need to be enrolled to the course and logged in to access the recording)
Lecture 4, 5, 10, 13 covers material which is part of the course content of 1RT003 only. Note that in the schedule the lectures are numbered according to the table above, also for 1RT705.
- [B] Christopher M. Bishop, Pattern Recognition and Machine Learning
. - [LWLS] Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön Machine Learning - A First Course for Engineers and Scientists Links to an external site.
- [KW] Diederik Kingma and Max Welling, An introduction to Variational Autoencoders Links to an external site.
NW = Niklas Wahlström
JS = Jens Sjölund
AR = Antonio Ribeiro
TS = Thomas Schön
Note that recorded lectures from previous year covers the material in different order and there might admin info in these which might be obsolete.
* = Bishop explain approximate inference (using Expectation propagation) in graphs in a quite complicated manner. I will give a (hopefully) more intuitive version in the lecture.