Hia Ghosh and Raj Kumar Maity
MoWe 2:30PM - 3:45PM Engineering Lab II Room 119
Section AA: Fr 1:25PM - 2:15PM Marston Hall room 211
Section AB: Fr 10:10AM - 11:00AM Marston Hall room 211
Section AC: Fr 12:20PM - 1:10PM Engineering Laboratory room 306
Section AD: Fr 11:15AM - 12:05PM Engineering Laboratory room 306
Instructor: Tu 10:00AM-11:00AM CS 332
Raj: Mo 11:00 AM - 12:00 noon (LGRC A303)
Hia: We 11:00 AM - 12:00 noon (LGRC A349)
Required: Introduction to Probability, 2nd Edition by Dimitri P. Bertsekas and John N. Tsitsiklis.
There will be 4 home assignments, 2 in class midterms and 1 final exam.
Participation (Discussion section activities, random in-class quizzes) 10%
Homeworks (including occasional Moodle quiz very week or so) 25%
Midterm 1 20%
Midterm 2 20%
Final 25%
Assignments must be submitted at the dropbox in CS main office by 4 pm of the deadline. Late submission by a day at the instructors office will incur a 20% penalty. Submissions will not be accepted if there is any more delay without substantial reason (such as a doctor's note).
Non-programming assignments of 240 are for individual assessments. You should attempt to come up with a solution on your own. However you may discuss assignments with other students and instructor/TAs in Piazza in case of any trouble understanding the setting. If you are discussing assignments in person with someone to come up with an answer, you should indicate that clearly in your write-up. In any case, you must write your own solution, and in your write-up you must show that you fully understand the solution.
| Lecture | Date | Topics | Notes |
| 1 | We Sep 7 | Logistic of the course, introduction, set theory | Lecture 1 |
| 2 | Mo Sep 12 | Probability axioms, Conditional Probability | Lecture 2 |
| 3 | We Sep 14 | Sequential Models, Bayes’ Rule | Lecture 3 |
| 4 | Mo Sep 19 | Bayes’ Rule, Independence, Conditional Independence | Lecture 4 |
| 5 | We Sep 21 | Counting, Binomial Law | Lecture 5 |
| 6 | Mo Sep 26 | Discrete Random Variables | Lecture 6 |
| 7 | We Sep 28 | Expectation | Lecture 7 |
| 8 | Mo Oct 3 | Functions of Random Variables | Lecture 8 |
| 9 | We Oct 5 | Variance | Lecture 9 |
| 10 | Mo Oct 10 | Midterm 1 | Exam |
| 11 | We Oct 12 | Multiple Random variables | Lecture 10 |
| 12 | Mo Oct 17 | Conditional PMFs, Entropy | Lecture 11 |
| 13 | We Oct 19 | Data Transmission | Lecture 12 |
| 14 | Mo Oct 24 | Data Compression | Lecture 13 |
| 15 | We Oct 26 | Markov and Chebyshev Inequalities | Lecture 14 |
| 16 | Mo Oct 31 | Concentration Inequalities and Covariance | Lecture 15 |
| 17 | We Nov 2 | Correlation, Continuous Random Variables | Lecture 16 |
| 18 | Mo Nov 7 | Revision | Lecture 17 |
| 19 | We Nov 9 | Midterm 2 | Exam |
| 20 | Mo Nov 14 | Continuous Random Variables, probability densities, Exponential, Gaussian Random Variables | Lecture 18 |
| 21 | Mo Nov 28 | Markov Chains, State Transition, Steady State | Lecture 19 |
| 22 | We Nov 30 | Markov Chain, Irreducible, Periodic, Steady State Theorem | Lecture 20 |
| 23 | We Dec 5 | Bayesian Network | Lecture 21 |
| 24 | We Dec 7 | BayesNet from Data, Hypothesis Testing | Lecture 22 |
| 25 | We Dec 12 | Hypothesis Testing, Game Theory | Lecture 23 |
| 26 | We Dec 14 | Review of Course | Lecture 24 |
First Midterm Oct 11, 2016 In Class. Syllabus: Up to Lecture 9; Up to Section 2.4 (inclusive) of Textbook (BT).
Second Midterm Nov 9, 2016 In Class. Syllabus: Up to Lecture 16.
Closed book exam; Bring pen; Basic/Scientific Calculators (that do not have any other functionality beyond what is available in TI-30xa) are allowed; Use of any other electronic device of any kind is not allowed; Discussions during the exam is not allowed; Cheating in the exam will result in a grade of 0 as well as a report in the undergraduate office (standard University Ethics Code applies).
Solutions of the Exams will only be posted in Moodle.
See Moodle.
University Schedule: Friday Dec 16, 2016 3:30PM