MW, 5:30-6:50 P.M. in Math 4-130
Kevin Sackel, kevin.sackel(at)stonybrook.edu, Math 3-118
Office Hours:
Saeed Ghahramani's Fundamentals of Probability with Stochastic Processes, 3rd Edition (NB: Not the 4th edition.)
Jordan Rainone
Office Hours: Thursday 2:30-3:30 in 3-106
Homework 1, due September 4, the Solutions, and a histogram of counts from Problem 0.5
Homework 2, due September 11, and the Solutions
Homework 3, due September 23, and the Solutions
Homework 4, due October 2, and the Solutions
Homework 5, due October 9, and the Solutions
Homework 6, due October 16, and the Solutions
Homework 7, due October 30, and the Solutions
Homework 8, due November 14, and the Solutions
Homework 9, due November 20, and the Solutions
Homework 10, due December 2, and the Solutions
Homework 11, due December 9, and the Solutions
Mid-term exam, October 21, in class 5:30-6:50 P.M.
Practice Midterm A, and the Solutions
Practice Midterm B, and the Solutions
Midterm Exam, and the Solutions
Final exam, December 16, 5:30-8:00 P.M.
Practice Final, and the Solutions
Final Exam, and the Solutions
Challenge Problems (Solutions available upon request)
Lecture 1 (M, Aug 26): (1.2,1.3) The language of probability - sets and their relations, sample spaces, events, and probability
Lecture 2 (W, Aug 28): (1.4,1.7, 2.1) Basic results and examples; counting principle
*No class on M, Sept 2 (Labor Day)*
Lecture 3 (W, Sept 4): (2.2, 2.3) More counting principles, permutations
Lecture 4 (M, Sept 9): (2.4) Combinations
Lecture 5 (W, Sept 11): (2.4 (cont), 3.1) Binomial expansion, conditional probability
Lecture 6 (M, Sept 16): (3.2, 3.3) Law of Total Probability
Lecture 7 (W, Sept 18): (Extra; 3.4) Monty Hall Problem; Bayes' Theorem
Lecture 8 (M, Sept 23): (3.5) Independence
Lecture 9 (W, Sept 25): (4.1) Random variables; some examples
Lecture 10 (M, Sept 30): (4.2, 4.3) Distribution functions; operations on random variables; discrete random variables; probability mass function
Lecture 11 (W, Oct 2): (4.4) Expected values; Law of the Unconscious Statistician
Lecture 12 (M, Oct 7): (4.5, 4.6) Variance; standardization
Lecture 13 (W, Oct 9): (5.1) Bernoulli Random Variable; Binomial Random Variable (plus examples: p=1/2, p=1/3, p=1/8)
*No class on M, Oct 14 (Fall break)*
Lecture 14 (W, Oct 16): Review!
**M, Oct 21: MIDTERM EXAM (In class)**
Lecture 15 (W, Oct 23): (5.2, 5.3) Poisson Random Variable (plus examples: lambda = 1, 2, 3, 10); Geometric Random Variable
Lecture 16 (M, Oct 28): (6.1) Calculus review; continuous random variables; probability density function
Lecture 17 (W, Oct 30): (6.3, 7.1, 7.2) Continuous random variables; Uniform Random Variable, Standard normal
Lecture 18 (M, Nov 4): (7.2) Normal distribution
Lecture 19 (W, Nov 6): (7.2, 7.3) DeMoivre-Laplace Theorem; Exponential Random Variable
Lecture 20 (M, Nov 11): (7.3, 8.1) Memorylessness of Poisson process; Joint probability mass
Lecture 21 (W, Nov 13): (8.1, 8.2, 8.3) Joint probability mass; Conditional probability for joint probability mass; Independent random variables; Expectations of sums; Expectations of products for independent random variables
Lecture 22 (M, Nov 18): (8.3) More conditional probability
Lecture 23 (W, Nov 20): (Remarks on Chapter 9, 10.1) Expected values of sums of random variables (NOTE: first two pages of this scan are non-examinable, and we didn't cover random samples and i.i.d. random variables in class - we will come back to this in the last two lectures)
Lecture 24 (M, Nov 25): (10.1, 10.2, 10.3) Cauchy-Schwarz Inequality for random variables; Covariance; Correlation coefficient
*No class on W, Nov 27 (Thanksgiving)*
Lecture 25 (M, Dec 2): (10.2, 10.3) More on covariance and correlation
Lecture 26 (W, Dec 4): (Selections from Chapter 11 (non-examinable))
Lecture 27 (M, Dec 9): Review!
*No class on W, Dec 11 (Finals period)*
**W, Dec 16: FINAL EXAM 5:30-8:00 PM**