CSE 312,  Wi '17:  Approximate Schedule
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Schedule details will evolve as we go; check back periodically to see the latest updates.

Week 1
1/2-1/6
W   Introduction                                                   Berteskas Ch 1
F Counting: combinations, permutations, etc.
Week 2
1/9-1/13
M   Probability: Axioms, Conditional Probability, Bayes, Independence, etc.
W HW #1
F (notes)
Week 3
1/16-1/20
M Holiday
W HW #2 Probability: Axioms, Conditional Probability, Bayes, Independence, etc.
F (notes)
Week 4
1/23-1/27
M (notes)
W (notes) HW #3 Discrete Random Variables, PMFs, Expectation, Variance, Joint Distributions Bertsekas Ch 2
F (notes)
Week 5
1/30-2/3
M (notes)
W (notes) HW #4
F (notes)
Week 6
2/6-2/10
M   Snow Day
W (notes) Midterm Review
F Midterm
Week 7
2/13-2/17
M (notes)   Discrete Random Variables, PMFs, Expectation, Variance, Joint Distributions
W (notes) HW #5
F   Midterm
Week 8
2/20-2/24
M Holiday
W (notes) HW #6 Continuous Random Variables, CDFs, PDFs, Normal Distribution Bertsekas Ch 3
F (notes)
Week 9
2/27-3/3
M (notes)   Max Likelihood Estimators, EM Bertsekas, 9.1. Supplementary reading below.
W (notes) HW #7
F (notes)   Tails and Limit Theorems Bertsekas Sect 4.4 & Ch. 5
Week 10
3/6-3/10
M (notes)
W (notes) HW #8
F   Wrap up & Review
Week 11
3/13-3/17
M

Final Exam
2:30-4:20 Mon, March 13, 2017

Textbooks:

Required:

Introduction to Probability (2nd edition), Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008(Available from U Book Store, Amazon, etc.)

Reference. (No direct use of this, but if you already own a copy, keep it for reference. Some students have said they like its coverage of counting (Chapter 5 and 7.5, 7.6) and discrete probability (Chapter 6). Editions other than the sixth are probably equally useful.):

Discrete Mathematics and Its Applications, (sixth edition) by Kenneth Rosen, McGraw-Hill, 2006. Errata. (Available from U Book Store, Amazon, etc.)

In addition to the assigned text, there are many supplementary resources available on the web and elsewhere that may be helpful. Here are a few. I welcome hearing about others that you discover.

1. Rosen (above) covers counting (Chapter 5 and 7.5, 7.6) and discrete probability (Chapter 6).
2. Some previous versions of this course used A First Course in Probability (8th edition), Sheldon M. Ross, Prentice Hall, 2009. (Available from Odegaard QA273 .R83 2010, Amazon, etc.) Ross has many good examples, exercises, reasonable level of math; perhaps a little less good on overview/intuition. See a previous quarter's web page for detailed readings approximately corresponding to this quarter's coverage.
3. Wikipedia covers many of the same topics. Its coverage can be uneven and/or too advanced, but much is useful. E.g., here are some specifics on likelihood:
4. Wolfram's MathWorld, (by E.W. Weisstein), also covers much of this ground, but again not always at the right level for beginners. One specific topic that might be of interest:
5. If you want more detail on EM and model-based clustering:
6. The "Chance Project".
7. Introduction to Probability by Charles Grinstead and Laurie Snell
The open access textbook for the Chance project. Roughly comparable in coverage to Ross or Bertsekas, but with a different slant, of course.
8. Virtual Laboratories in Probability and Statistics
9. Wikibooks: Probability
10. Wikibooks: Statistics
11. Wikiversity: Statistics
12. Statistics Online Computational Resource
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