|Ti 16-18||D122||Pekka Parviainen||17.01.2012-23.02.2012|
|To 16-18||D122||Pekka Parviainen||17.01.2012-23.02.2012|
Information for international students
The course will be held in English.
This course belongs to the Algorithms and Machine Learning sub-pogramme in the Master's programme of the department, and together with 582637 Project in Probabilistic Models (2 cr), it forms one of the three optional courses of the sub-programme.
For students in the old Intelligent Systems specialisation area: this course replaces, together with the project work 582637 Project in Probabilistic Models (2 cr), the course Three Concepts: Probability (6 cr).
The format of the course in Spring 2012 follows roughly the format of the course in 2011.
The course is an introductory course, and only elementary knowledge on probability theory is required as a prerequisite. Different parts of the course, however, have different requirements with respect to the mathematical machinery needed to apply the concepts in question. Typically some analysis and elementary mathematical statistics is required. At the very least we assume that the participants are familiar with topics covered in the courses 582630 Design and analysis of algorithms (4 cr) and 582631 Introduction to machine learning (4 cr).
There will be weekly exercises and a final exam. Solutions to the weekly exercises need to be delivered to the course assistant before each exercise session (by paper or by email).
The maximum number of points that can be earned from the exercises is 12 and from the course exam 48, so the total maximum is 60 points (and you need 30 points in total and 24 points from the exam to pass the course). The course exam will be held on 27.2. 16.00-18.30 (a calculator is allowed but no other electronic devices or written material).
Grades are now available.
Please give feedback (especially verbal comments are welcome).
Tue 17.1. Overview of the course, administrative issues. Lecture notes I: 1-40.
Thu 19.1. Refresher in probability. Lecture notes II: 1-28.
Tue 24.1. Bayesian inference. Lecture notes III:1-46.
Thu 26.1. Bayesian inference continues (Lecture notes III:47-53). The Bayesian network representation. Lecture notes IV:1-19 (updated 2.2.)
Tue 31.1. The Bayesian network representation (Lecture notes IV:20-37) .
Thu 2.2. The Bayesian network representation (Lecture notes IV:38-55).
Tue 7.2. Inference in Bayesian networks. Lecture notes V: 1-27 (updated 15.2.).
Thu 9.2. Inference in Bayesian networks. Lecture notes V:28-47, Lecture notes VI:1-3 (updated 15.2.).
Tue 14.2. Inference in Bayesian networks continues (Lecture notes VI:4-24).
Tue 21.2. Learning Bayesian networks continues. Lecture notes IX:1-22 (updated 23.2.).
Thu 23.2. Learning Bayesian networks continues (Lecture notes IX:23-39).
Kirjallisuus ja materiaali
The primary material is the lectures notes. All lecture notes in one file.
- Material of the year 2011 course
- Material of the year 2010 course.
- David Barber: Bayesian Reasoning and Machine Learning, parts I and II.
- Richard E. Neapolitan: Learning Bayesian Networks.
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall, 2003. Chapters 13, 14, (15, optional), 20.1-20.3
- Judea Pearl: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.
- Daphne Koller and Nir Friedman: Probabilistic graphical models. MIT Press, 2009.
|To 14-16||B119||Teppo Niinimäki||23.01.2012-24.02.2012|