Probabilistic Models

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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.

Koe

24.02.2014 16.00 A111
Vuosi Lukukausi Päivämäärä Periodi Kieli Vastuuhenkilö
2014 kevät 14.01-20.02. 3-3 Englanti Brandon Malone

Luennot

Aika Huone Luennoija Päivämäärä
Ti 16-18 D122 Brandon Malone 14.01.2014-20.02.2014
To 16-18 D122 Brandon Malone 14.01.2014-20.02.2014

Harjoitusryhmät

Group: 1
Aika Huone Ohjaaja Päivämäärä Huomioitavaa
Ke 16-18 C222 Quan Nguyen 20.01.2014—21.02.2014

Yleistä

Please see the course syllabus.

 

The course learning objectives are available here.

 

A (tentative) detailed description of the lectures is available here.

 

A (tentative) set of objectives for each of the exercise sets is available here.

 

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Kirjallisuus ja materiaali

Materials for Class

Tuesday, January 16: Introduction to Probabilistic Models: Slides

Thursday, January 18: Refresher on Probability Theory: Slides

Note: Kevin deLaplante (http://www.youtube.com/user/PhilosophyFreak/videos) has many more introductory videos that may be helpful.

Tuesday, January 21: Bayesian Networks: Slides, Class work 1 solutions,  d-Separation handout

Thursday, January 23: Special Models (Naive Bayes and Hidden Markov Models): Naive Bayes Classifiers Slides, Document Classification Handout, (Partial) Document Classification Solutions

Note: In this class, we will review d-separation and discuss naive Bayes classifiers.  We will discuss hidden Markov models and the forward-backward algorithm next Tuesday, January 28.

Tuesday, January 28: Special Models (Hidden Markov Models): Hidden Markov Models Slides, Gene Finding Handout, Gene Finding Handout Solutions

Thursday, January 30: Inference by Factors Elimination: Factor Elimination Slides

Note: Much of the material posted for today will also be relevant when discussing the junction tree algorithm on Tuesday, February 4.

Tuesday, February 4: Inference in General Bayesian Networks (Jointree Algorithm, also called the junction tree algorithm): Jointree Slides, Asia Network Handout, Asia Network Handout Solutions

Thursday, February 6: Multinomial Parameter Estimation: Parameter Estimation with Complete Data Slides, Parameter Estimation Handout, (Partial) Parameter Estimation Handout Solutions

Tuesday, February 11: Scoring Functions for Structure Learning: Scoring Functions Slides, Scoring Functions Handout, Scoring Functions Handout Solutions

Thursday, February 13: Algorithms for Score-based Structure Learning: Structure Learning Slides, Structure Learning Handout, Structure Learning Handout Solutions

Tuesday, February 18: Expectation-Maximization and Poisson Mixture Models: Poisson Mixture Model slides, Poisson Mixture Models Handout, Poisson Mixture Models Handout Solutions

Thursday, February 20: Topic Models, A Recent Success Story: Topic Models Slides

Lab Exercises

Exercise grades (Updated with Set 5 and Total Points)

Wednesday, January 22: Foundations of Probability, 3 points

  • Exercise set 1
  • The LaTex file
  • Solutions

Wednesday, January 29: Bayesian Networks and Naive Bayes Classifiers, 5 points

  • Exercise set 2
  • The LaTex file
  • Solutions, NBC.R

Wednesday, February 5: Hidden Markov Models and Inference by Factor Elimination

  • Exercise set 3
  • The LaTex file
  • Solutions, HMM.R, ET.R

Wednesday, February 12: Inference with Jointrees and Parameter Estimation with Complete Data

  • Exercise set 4
  • The LaTex file
  • Solutions, jointree.R

Wedensday, February 19: Scoring Functions and Structure Learning

  • Exercise set 5
  • The LaTex file
  • Solutions, BN.R

LaTex References

The Wikibook on LaTex is quite thorough and well organized.  In particular, the Mathematics section may be helpful.

Stack Overflow and the relevant section in Stack Exchange are also good resources, although it is often easiest to search using Google.

Additional Material