CSE 312 Winter 2020
Lecture Topics

Subscribe to this calendar (Google, iCal, etc.)

January 6 Overview; Counting: product rule
Reading: BT 1.1, 1.2, 1.6, Intro to Schnapsen, Rules of Schnapsen
Notes, HW1 LaTeX source
January 8 Counting: permutations
Reading: Safety First (Schnapsen analysis)
Trick mechanics, Notes
January 10 Counting: combinations, complementing
January 13 Counting: binomial theorem, inclusion-exclusion, pigeonhole principle
Reading: BT 1.3
Notes, HW2 LaTeX source
January 15 Intro to probability, equally likely outcomes
January 17 Conditional probability
Reading: BT 1.5
January 22 Law of Total Probability, Bayes' Theorem
Reading: BT 1.4
Notes, HW3 LaTeX source
January 24 Independent events
Reading: BT 2.1-2.3
January 27 Gambler's Ruin, Naive Bayes classifier
Reading: Naive Bayes notes
Slides (PDF, PPTX), Notes
January 29 Random variables, expectation
Reading: BT 2.4, Expected Game Points, previous year's exercise
Notes, HW4 LaTeX source
January 31 Geometric random variable, linearity of expectation
Reading: BT 2.7
February 3 Variance, independent random variables
February 5 Uniform, Bernoulli, binomial distributions
Reading: BT 3.1-3.2
Notes, Slide pack 6 slide 67
February 7 Error-correcting codes, Poisson distribution
Slide pack 6 slides 68-80, general Hamming code, HW5 LaTeX source
February 10 Continuous random variables
Reading: BT 3.3
February 14 Uniform, exponential distributions
Reading: BT 7.4
Notes, HW6 LaTeX source
February 19 Midterm debriefing, normal distribution
Slide pack 7, slides 20-24, Notes
February 21 Central Limit Theorem
Slide pack 7, slides 24-33, Demo, Notes, HW7 LaTeX source
February 24 Approximating binomial via Central Limit Theorem, continuity correction
Reading: BT 7.1
Slide pack 10, slides 30-42
February 26 Markov and Chebyshev inequalities
Reading: BT 7.2, 7.5
February 28 Chernoff Inequality, law of large numbers
Reading: Maximum likelihood estimators
Slide pack 10, slides 14-19, Notes
March 2 Maximum likelihood estimators
Reading: Bias and confidence intervals
Notes, HW8 LaTeX source
March 4 Maximum likelihood estimators for normal distribution; bias
March 6 Confidence intervals
March 9 Probabilistic algorithms: primality, quicksort
March 11 Freivalds' algorithm
Freivalds' algorithm slides
March 13 Doktor Schnaps live: watch the Maestro get crushed!
Where Doktor Schnaps implements randomization