Probabilistic Models

Algoritmit ja koneoppiminen
Syventävät opinnot
This course provides an introduction to probabilistic modeling from a computer scientist"s perspective. Many of the research issues in Artificial Intelligence, Computational Intelligence and Machine Learning/Data Mining can be viewed as topics in the "science of uncertainty," which addresses the problem of optimal processing of incomplete information, i.e., plausible inference, and this course shows how the probabilistic modeling framework forms a theoretically elegant and practically useful solution to this problem. The course focuses on the "degree-of-belief" interpretation of probability and illustrates the use of Bayes" Theorem as a general rule of belief-updating. As a concrete example of methodological tools based on this approach, we will study probabilistic graphical models focusing in particular on (discrete) Bayesian networks, and on their applications in different probabilistic modeling tasks.


02.03.2011 16.00 A111
Vuosi Lukukausi Päivämäärä Periodi Kieli Vastuuhenkilö
2011 kevät 18.01-24.02. 3-3 Englanti Petri Myllymäki


Aika Huone Luennoija Päivämäärä
Ti 16-18 B222 Petri Myllymäki 18.01.2011-24.02.2011
To 16-18 B222 Petri Myllymäki 18.01.2011-24.02.2011


Group: 1
Aika Huone Ohjaaja Päivämäärä Huomioitavaa
Ti 14-16 B222 Sourav Bhattacharya 24.01.2011—25.02.2011

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 2011 follows pretty closely to the format of the course in 2009, not the format of the course as it was in 2010.

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).

Kurssin suorittaminen

There will be weekly exercises, and one larger home assignment to be delivered after the course exam. Solutions to the weekly exercises need to be delivered by email before each exercise session. The deadline for the home assignment (to be delivered by email to the course instructor AND the teaching assistant) is Monday, March 7, at 8:00 in the morning. B-Course crashed on Saturday 5th. The new deadline is Thursday, March 10, at 08:00 in the morning.

The maximum number of points that can be earned from the exercises is 12, from the home assignment 12, and from the course exam 36, so the total maximum is 60 points (and you need 30 points to pass the course). Attending the exercise sessions is not compulsory, but it is highly recommended because:

  • the main idea with the exercises is to learn, and the ecercise session  offers an excellent opportunity for this!
  • your exercise points will be determined based on your submitted solutions, but they won't be graded before the final exam, so if you want to know how well you are doing, you need to attend the exercise
  • the exercises are good preparation for the final exam (if you have made erros in your solutions, the only way to discover about them is to attend the exercise session, where you can also leanr the correct solutions)


Home Assignment

Description of the task can be found on the corresponding sub page of this page. The deadline for submitting the necessary documents is Monday, March 7, 2011, at 08:00am. . B-Course crashed on Saturday, was up for a while, but gives now a Python error message. We are working on the problem.  but is up again. The deadline has been extended to Thursday, March 10, at 08:00am.

Note that the work done for the home assignment will give you a good starting point for the separate project work course 582637 Project in Probabilistic Models (2 cr) to be held in the next period.


Course Schedule

  • Tue 18.01. at 16-18: Overview of the course, administrative issues, lecture slides I:1-27
  • Thu 20.01. at 16-18: Lecture slides I:28-81.
  • Tue 25.01. at 14-16: No exercises.
  • Tue 25.01. at 16-18: Lecture slides II:1-36.
  • Thu 27.01. at 16-18: Lecture slides III:1-27.
  • Tue 01.02. at 14-16: Exercises 1-3.
  • Tue 01.02. at 16-18: Lecture slides III:28-44, IV:1-10
  • Thu 03.02. at 16-18: Lecture slides IV:10-38.
  • Tue 08.02. at 14-16: Exercises 4-7.
  • Tue 08.02. at 16-18: Lecture slides IV:39-50.
  • Thu 10.02. at 16-18: Introduction to the home assignment. Q&A about the course material covered so far (including exercises).
  • Tue 15.02. at 14-16: Exercises 8-9.
  • Tue 15.02. at 16-18: Lecture slides Lecture slides IV:51-60  and  V:1-22.
  • Thu 17.02. at 16-18: Lecture slides V:23-37.
  • Tue 22.02. at 14-16: Exercises 10-12.
  • Tue 22.02. at 16-18: Lecture slides V:38-46, VI:1-15.
  • Thu 24.02. at 16-18: Lecture slides VI:16-25.
  • Wed. 02.03 at 16-20: the course exam
  • Mon 07.03 at 08:00: Thu 10.03 at 08:00; deadline for the  home assignment.


Course Results

Results can now be found here. If you want to discuss your grade, please contact the course instructor.