COMPSCI 240 (Reasoning Under Uncertainty)


Arya Mazumdar

Teaching Assistants

Hia Ghosh and Raj Kumar Maity


MoWe 2:30PM - 3:45PM Engineering Lab II Room 119


  1. Section AA: Fr 1:25PM - 2:15PM Marston Hall room 211

  2. Section AB: Fr 10:10AM - 11:00AM Marston Hall room 211

  3. Section AC: Fr 12:20PM - 1:10PM Engineering Laboratory room 306

  4. Section AD: Fr 11:15AM - 12:05PM Engineering Laboratory room 306

Office Hour

Class Webpages

  1. This webpage

  2. Piazza

  3. Moodle


  1. Required: Introduction to Probability, 2nd Edition by Dimitri P. Bertsekas and John N. Tsitsiklis.

Grading Plan

There will be 4 home assignments, 2 in class midterms and 1 final exam.

Homework Plan

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.

Programming Practice

See Moodle.


University Schedule: Friday Dec 16, 2016 3:30PM