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Lecturer: | Jaakko Hollmén, D.Sc.(Tech.) |
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Course Assistant: | Tapani Raiko, D.Sc.(Tech.) |
Semester: | Spring term, periods III and IV |
Credit points: | 5 ECTS credit points, also eligible for post-graduate studies |
Lectures: | Lecture hall T3, Thursdays 10-12 (starting on the 17th of January) |
Exercises: | Lecture hall T3, Fridays 10-12 (starting on the 25th of January) |
Language: | English |
Course book: | Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer, 2006. |
Homepage: | http://www.cis.hut.fi/Opinnot/T-61.5140/ |
Contents: | The course covers probabilistic concepts in machine learning starting with the concepts of independence and conditional independence as the basis for subsequent models. During the course, we will study probabilistic modeling with Naive Bayes type of models and finite mixture models and extend the treatment to Bayesian networks. Learning of the model parameters is covered in the framework of maximum likelihood. Computational algorithms for exact and approximate inference will be covered. Other topics include sampling and the use of prior information. |
Time | Topic on the lecture | Materials | Exercises | Materials |
---|---|---|---|---|
17.1. | Introduction to the course and the topic | Lecture notes | ||
24.1. | Graphical Models | Bishop: Chapter 8 | 25.1. | problems, solutions |
31.1. | Graphical Models, cont. | Bishop: Chapter 8 | 1.2. | problems, solutions, code |
14.2. | Graphical models, cont. | Lecture notes | 7.2. | problems, solutions |
15.2. | Self-study lecture about graphical models | Self-study problems | Useful formulae (ver 1) | |
21.2. | Winter holiday, no lecture | N/A | 22.2. | problems, solutions |
28.2. | Mixture models and EM | Bishop, Chapter 9 Lecture notes |
29.2. | problems, solutions |
6.3. | No lecture, examination period | N/A | no (7.3.) | N/A |
13.3. | Mixture Models and EM, cont. | Bishop, Chapter 9 | 14.3. | problems, solutions |
20.3. | No lecture, easter holiday | N/A | no (21.3.) | N/A |
27.3. | Sequential Data | Bishop, Chapter 13 Term project |
28.3. | problems, solutions |
3.4. | Approximate inference, (Sampling) | Bishop, Chapter 11 | 4.4. | problems, unknown_p.m, gaussian.m |
10.4. | Approximate inference | Bishop, Chapter 11 | 11.4. | solutions, matlab code, BUGS demo |
17.4. | Variational Bayesian learning | Bishop Chapter 10, Lecture notes |
18.4. | problems, solutions |
24.4. | Run-through of the course Exam requirements |
Exam requirements | 25.4. | problems, solutions |
In the term project, you will use the software package BernoulliMix to solve real machine-learning problems using ready-made implementation of finite mixture models of multivariate Bernoulli distributions.
The deadline for the term project work is May 31st. If you are a visiting student, and return your report earlier, you will get feedback before your departure.You are at: CIS → T-61.5140 Machine Learning: Advanced Probabilistic Methods
Page maintained by t615140@cis.hut.fi, last updated Tuesday, 19-Aug-2008 10:51:03 EEST