Syllabus
EE 364: Introduction to Probability and Statistics for EE/CS
Spring 2026 (4 units)
This course introduces the main concepts of modern probability and statistics. This course focuses on reasoning with probabilistic uncertainty. The course depends primarily on lecture material. Attendance is mandatory.
Lecture: Tuesday, Thursday (section: 30713), 16:00 – 17:50
Discussion: Monday (section: 30715), 15:00 – 15:50
Enrollment is in-person ONLY. Attendance is mandatory to all lectures. Taping or recording lectures or discussions is strictly forbidden without the instructor’s explicit written permission.
Course materials
NOTE: Texts are secondary to in-class lecture material.
Required: Probability and Statistical Inference, 10th edition. Robert Hogg, Elliot Tanis, and Dale Zimmerman. Pearson Education. 2023.
Recommended: Probability, Statistics, and Random Processes for Electrical Engineering, Leon-Garcia, A., Pearson, 2008.
Recommended: Schaum’s Outline of Probability, Random Variables, and Random Processes, 4th edition. Hwei Hsu. McGraw-Hill Education. 2019.
“No AI” policy
The use of AI tools is strictly prohibited and considered a serious violation of academic integrity. This includes all artificial intelligence technologies, including but not limited to large language models, text generators, and AI-assisted writing software, for any aspect of coursework — be it research, outlining, drafting, editing, or proofreading. Any suspected use of AI will be thoroughly investigated. Violations may result in severe consequences, including course failure and referral to the Office of Student Judicial Affairs and Community Standards for further disciplinary action.
Course Outline
| Week | Topics |
|---|---|
| Week 1 13 Jan |
Introduction to probability. Logic and sets. Probability axioms. |
| Week 2 20 Jan |
Independence. Conditional probability. Bayes’ Theorem. |
| Week 3 27 Jan |
Combinatorics. Binomial and multinomial probability. |
| Week 4 03 Feb |
Discrete probability and mass functions. Poisson Theorem. |
| Week 5 10 Feb |
Midterm 1. Continuous probability densities. |
| Week 6 17 Feb |
Expectation, variance. Transformed random variables. |
| Week 7 24 Feb |
Multiple random variables. Covariance and correlation. Uncertainty principles. |
| Week 8 03 Mar |
Multivariate normal. Mixtures. Laws of large numbers. |
| Week 9 10 Mar |
Midterm 2. Bayesian statistics and conjugacy. |
| 17 Mar | No class, Spring Break. |
| Week 10 24 Mar |
Maximum likelihood estimation. Entropy. Monte Carlo sampling. |
| Week 11 31 Mar |
Transforms. Sample mean and sample variance. Central limit theorem. |
| Week 12 07 Apr |
Confidence intervals. Statistical hypothesis testing. |
| Week 13 14 Apr |
Midterm 3. Optimal estimation and least squares. |
| Week 14 21 Apr |
Linear regression and multivariable regression. |
| Week 15 28 Apr |
Logistic Regression. Probability structure of neural classifiers. Review. |
| Thursday 07 May |
Final Exam, 16:30 - 18:30 |
Grading Procedure
Class grade depends on the best two out of three midterms and the final exam. Homework problems are optional and worth at most 15 points of extra credit. But you should work these and many more problems than these to master the material.
Midterm Exams (50%)
There are three midterm exams. 60 minutes each. 25 points per exam. Cumulative. Closed book with no notes sheet. You are expected to bring a non-graphing scientific calculator. The midterm exam score sums your 2 best exam scores — dropping the exam with the lowest score. No make-up exams. Missed exams earn 0 points.
Final Exam (50%)
50 Points. Cumulative. The final exam is closed book with no notes sheet. You are expected to bring a non-graphing scientific calculator.
Homework Problems
Homework problems are checked but not graded. They count at most as 15 points if all homework sets are turned in and accurately worked. Homeworks are due by the posted due date. Late homework will not be accepted.
You may discuss homework problems with classmates but each student must submit their own original work. Turning in identical homework sets counts as cheating. Cheating warrants an F in the course.
Course Grade
A if 90.00 - 100 points,
B if 80.00 - 89.99 points,
C if 70.00 - 79.99 points,
D if 60.00 - 69.99 points,
F if 0 - 59.99 points.
(“+” and “–” at ≈ 1.5% of grade boundary).
Cheating
Cheating is not tolerated on homework or exams. Penalty ranges from F on exam to F in course to recommended expulsion.