6 EC
Semester 1, period 1
52042MAL6Y
| Owner | Master Artificial Intelligence |
| Coordinator | dr. P.D. Forré |
| Part of | Master Artificial Intelligence, |
| Links | Visible Learning Trajectories |
This course continues where Machine Learning 1 stopped. We will treat chapters 2, 8, 9, 10, 11, 13 of the book 'Pattern Recognition and Machine Learning' by C. Bishop, plus some additional material made clear on Canvas. Topics include:
C.M. Bishop: 'Pattern Recognition and Machine Learning'. Springer, 2006. ISBN 0-38-731073-8.
D.J.C. MacKay: 'Information Theory, Inference and Learning Algorithms'. CUP, 2003.
K.P. Murphy: 'Machine Learning: A Probabilistic Perspective'. MIT Press, 2012.
D. Barber: 'Bayesian Reasoning and Machine Learning'. CUP, 2012.
Lectures provide an overview of the material, reading the book helps understanding the details, practice/homework exercises and TA sessions let you test and develop your understanding of the theory and programming exercises develop your implementation skills.
|
Activity |
Hours |
|
|
Deeltoets |
0 |
|
|
Hoorcollege |
28 |
|
|
Laptopcollege |
0 |
|
|
Tentamen |
4 |
|
|
Vragenuur |
2 |
|
|
Werkcollege |
28 |
|
|
Self study |
106 |
|
|
Total |
168 |
(6 EC x 28 uur) |
This programme does not have requirements concerning attendance (OER part B).
| Item and weight | Details |
|
Final grade |
There will be (ungraded) practice exercises/homework, which will be discussed and practiced during the TA sessions (2 times per week).
There will be 4 tests in total, one every 2 weeks during the last TA session, lasting for about 1.5 hours and covering the content of the lectures up to the week before and corresponding practice exercises. The tests together will contribute the majority to the final grade.
There will be 3 graded lab assignments, which will also contribute to the final grade.
In case the student participates in the resit exam, the grade of the resit exam will replace all other grades to 100%.
See above.
All assignments have to be made individually. Feedback can be obtained via the TAs.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Weeknummer | Onderwerpen | Studiestof |
| 1 | exponential families, conditional independence, information theory, independent component analysis (ICA) |
Bishop: 1.6, 2.1-2.4, 8.2; |
| 2 | graphical models (Bayes nets, Markov random fields, factor graphs), (d-)separation, learning in graphical models | Bishop: 8.1-8.4.3; Murphy: 10.4.2 |
| 3 | exact inference in graphical models (variable elimination-, sum-product-, max-sum-algorithm, (loopy) belief propagation) | Bishop: 8.4; Murphy: 20.3 |
| 4 | variational inference: (general variational) expectation-maximisation (EM), variational auto-encoders (VAE), variational Bayes (mean-field) approximation (VB) | Bishop: 9-10; + paper |
| 5 | sequential data models (hidden Markov models, linear dynamical systems) | Bishop: 13.1-13.2 |
| 6 | sampling methods (MCMC and many others) | Bishop: 11.1-11.4; + other books |
| 7 | causality and misc. | books of Pearl and Peters et al. + other sources |
| 8 |
Prior knowledge: It is required to have successfully finished "Machine Learning 1" before one can register for "Machine Learning 2". The student should also be familiar with linear algebra, probability theory, calculus and programming in python.