Probability and AI - Syllabus, Spring 2002

80-316 and 80-616

http://www.andrew.cmu.edu/user/ps7z/syll2002.html

Instructor:  Peter Spirtes

Office:  135D BH

Telephone:  x88460

Office Hours:  T,Th 12:30-1:20

e-mail: ps7z@andrew.cmu.edu

Texts:  An Introduction to Bayesian Networks by F. Jensen

various articles handed out in class

for graduate students: Causality by Judea Pearl

60% Homework Assignments

20% Final

1.      Introduction

2.      Causation

Jensen 1.1.-1.2

http://www.phil.cmu.edu/courses/csr/ Causation Among Variables

Indeterministic Causation

3.      Causation –

– Causation – Causal graphs

Causation – Interventions

4.      Approaches to Uncertainty

5.      Probability I

Jensen, 1.3,

http://www.math.uah.edu/stat  Probability - Probability Spaces, 1 – 5

6.       Probability II

Jensen, 1.3,

http://www.math.uah.edu/stat/  Probability - Distributions, 1 – 6

7.      Probability III

Jensen, 1.3,

http://www.math.uah.edu/stat/  Probability – Expected Values, 1 – 3, 5 – 6

8.      Probability IV –

http://www.math.uah.edu/stat/   Probability – Probability Spaces, 6

Online Causal Course – Association – Independence and Association

Online Causal Course – Association – Conditional Independence

9.

10.    Bayesian Networks –

Jensen, 1.4.

http://developer.hugin.com/Getting_Started/ Bayesian Networks

11.    Causality and Probability

Causation to Association – Unconditional association

Causation to Association – Conditional

association

http://www.phil.cmu.edu/courses/csr/ Course – Causation to Association – d-

separation

12.    Constructing Bayesian Networks

Jensen, 2

http://developer.hugin.com/Getting_Started/  Tutorials – Building a Bayesian

Network

13.    Updating

Jensen, 5.7

handout

Hugin exercises

14.    Manipulating and Predicting

Interventions

15.    Parameter Estimation and Sampling Distributions

http://www.math.uah.edu/stat/ Random Samples, sections 1-6, 9

16.    Parameter Estimation and Sampling Distributions

http://www.math.uah.edu/stat/ Point Estimation, sections 1, 3-6

17.    Applications

18.    Other Approaches to Classification: Regression

handout

19.    Other Approaches to Classification

20.    Causal inference

http://www.phil.cmu.edu/courses/csr/ Association to Causation – Problems

with Causal Discovery

Association to Causation – Confounding

21.    Constructing Bayes Networks – Bayesian methods

22.    Constructing Bayes Networks: Constraint Based Methods

23.    Latent Variable Inference I

24.    Latent Variable Inference II