CS/SE 3341 Probability and Statistics in Computer Science

Instructor: Michael Baron                          Teaching Assistant: Marzana Chowdhury
Office: FO2.602-E Office: FO 1.210
Phone: 972-UTD-6874 Office hours:Monday 1-3 pm; Tuesday 2-5 pm

Course Syllabus - schedule, office hours, tips, policies

eLearning - check your grades and join discussion groups

MuchLearning - homework assignments. Follow MuchLearning instructions.

Tables of Distributions

The Final Exam is on May 8, 5:00 - 7:00 pm. Here is a PRACTICE FINAL


Homework is assigned on MuchLearning.

Quizzes and Exams

Quiz 1, solutions
Quiz 2, solutions
Quiz 3, solutions
Quiz 4, solutions
Midterm Exam, solutions
Quiz 5, solutions
Quiz 6, solutions
Quiz 7, solutions
Quiz 8, solutions
Quiz 9, solutions
Quiz 10, solutions


The Final Exam is on May 8, 5:00 - 7:00 pm. Here is a PRACTICE FINAL
Ready for more practice? Here are additional exercises.

The final exam covers topics: Notice that the 2nd part of the course is heavily based on the 1st part. So, practically, the exam is cumulative.

Here is Prof. Baron's Cheat Sheet for the Final Exam which will be attached to your exams along with the tables of distributions. No other material is allowed on the exam.


Learn how to manage exam stress.

Some Lecture Notes

(You may need Acrobat Reader to see and print these notes)

Notes 1 "Introduction. Probability rules."
Notes 2 "Equally likely outcomes. Conditional probability"
Notes 3 "Random variables and distributions"
Notes 4 "Discrete distributions"
Tables of Distributions
Notes 5 "Continuous distributions"
Notes 6 "Important continuous distributions" (updated on Feb 28)
Notes 7 "Stochastic processes. Bernoulli, Binomial, Poisson processes."
Notes 8 "Markov chains"
Notes 9 "Single-server queuing systems"
Notes 10 "Statistical inference"

Additional Class Notes

Notes on Probability Rules
Notes on marginal and joint distributions
Notes on expected value and variance
Notes on discrete distributions
Notes on Binomial, Geometric, and Poisson
Examples on Binomial and Poisson distributions
Notes on Continuous Distributions
Examples on Normal distribution and Central Limit Theorem
Examples on Normal approximations and Binomial process
Example of a Markov chain
Bernoulli single-server queuing system
Markov chains and queuing systems examples
M/M/1 queuing system
Statistics: method of moments
Method of moments and Maximum likelihood
Estimation examples and Confidence Intervals
Hypothesis testing and confidence intervals
Final Review

Matlab corner

  • MATLAB programs used in our classroom demonstrations:
    Markov chain for sunny and cloudy days
    Markov chain for the game of ladder
    Poisson process of arrivals
    Bernoulli and Binomial processes
    Brownian motion
    Central Limit Theorem
  • A Matlab tutorial

    Recommended texts

    These texts overlap, so you don't need to buy all of them. See the course syllabus for their comparison and coverage.