EE 364: Introduction to Probability and Statistics for EE/CS
University of Southern California, Spring 2026
Schedule
Week 1
| Jan 13 |
Homework 1
Homework 1
|
Week 2
| Jan 20 |
Lecture 2
Independence. Conditional
probability. Bayes' Theorem.
Homework 2
Homework 2
|
Week 3
| Jan 27 |
Lecture 3
Combinatorics. Binomial and
multinomial probability.
Homework 3
Homework 3
|
Week 4
| Feb 3 |
Lecture 4
Discrete probability and mass
functions. Poisson Theorem.
Homework 4
Homework 4
|
Week 5
| Feb 10 |
Exam Midterm 1
Lecture 5
Continuous probability
densities.
Homework 5
Homework 5
|
Week 6
| Feb 17 |
Lecture 6
Expectation, variance. Transformed
random variables.
Homework 6
Homework 6
|
Week 7
| Feb 24 |
Lecture 7
Multiple random variables. Covariance
and correlation. Uncertainty principles.
Homework 7
Homework 7
|
Week 8
| Mar 3 |
Lecture 8
Multivariate normal. Mixtures. Laws
of large numbers.
Homework 8
Homework 8
|
Week 9
| Mar 10 |
Exam Midterm 2
Lecture 9
Bayesian statistics and
conjugacy.
Homework 9
Homework 9
|
No class, Spring Break.
Week 10
| Mar 24 |
Lecture 10
Maximum likelihood estimation.
Entropy. Monte Carlo sampling.
Homework 10
Homework 10
|
Week 11
| Mar 31 |
Lecture 11
Transforms. Sample mean and sample
variance. Central limit theorem.
Homework 11
Homework 11
|
Week 12
| Apr 7 |
Lecture 12
Confidence intervals. Statistical
hypothesis testing.
Homework 12
Homework 12
|
Week 13
| Apr 14 |
Exam Midterm 3
Lecture 13
Optimal estimation and least
squares.
Homework 13
Homework 13
|
Week 14
| Apr 21 |
Lecture 14
Linear regression and multivariable
regression.
Homework 14
Homework 14
|
Week 15
| Apr 28 |
Lecture 15
Logistic Regression. Probability
structure of neural classifiers. Review.
|
Final Exam - Thursday, May 7, 16:30 - 18:30