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  CSE 312,  Sp '15:  Approximate Schedule
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Schedule details will evolve as we go; check back periodically to see the latest updates.

    Due Lecture Topic Reading
Week 1
3/30-4/3		 M   Introduction                                                   Berteskas Ch 1
W Counting: combinations, permutations, etc. Probability: Axioms, Conditional Probability, Bayes, Independence, etc.
F
Week 2
4/6-4/10		 M  
W  
F HW #1
Week 3
4/13-4/17		 M  
W  
F HW #2 Discrete Random Variables, PMFs, Expectation, Variance, Joint Distributions Bertsekas Ch 2
Week 4
4/20-4/24		 M  
W  
F HW #3
Week 5
4/27-5/1		 M  
W   Continuous Random Variables, CDFs, PDFs, Normal Distribution Bertsekas Ch 3
F HW #4
Week 6
5/4-5/8		 M  
W   Midterm Review  
F   Midterm
Week 7
5/11-5/15		 M   Analysis of Algorithms
W   Tails and Limit Theorems Bertsekas Sect 4.4 & Ch. 5
F HW #5
Week 8
5/18-5/22		 M  
W   Max Likelihood Estimators, EM, Hypothesis/Significance Testing Bertsekas, 9.1, 9.3, 9.4. Supplementary reading below.
F HW #6
Week 9
5/25-5/29		 M Holiday
W   Max Likelihood Estimators, EM, Hypothesis/Significance Testing
F HW #7
Week 10
6/1-6/5		 M  
W  
F HW #8 Wrap up & Review
Week 11
6/8-6/12		 M

Final Exam
2:30-4:20 Monday, June 8, 2015

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)):

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

Supplementary Reading:

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 U Book Store, 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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