Probability and Random Processes
TuTh 12:30-2 PM, Lewis 100
OH: Tuesday 2-3 Cory 212
- Lab 2 Sols are released. Self grades are due by Friday night (2/21) via this form.
- Homework 4 is released. It is due by Wednesday night (2/19) at 11:59PM.
- Homework 3 Sols are released. Self grades are due by Wednesday night (2/19) via this form.
- Midterm 1 logistics have been posted on Piazza. Check out the exams page for more information and resources.
- Discussions start this week (1/21-1/24), OH starts next week (1/27-1/31).
- Welcome to EECS 126! Please read the course info, join Piazza, and join Gradescope (code 9XJ64Z).
Lectures are not recorded. Subject to some changes.
|1/21||Introduction, Probability Spaces, Conditional Probability, Law of Total Probability||B-T 1||HW 1
HW 1 Sol
|1/23||Independence, Bayes Rule, Discrete Random Variables||B-T 1, 2||Lab 0
Lab 0 Sol
|1/28||Expectation, Uniform, Geometric, Binomial and Poisson Distributions||B-T 2||HW 2
HW 2 Sol
|1/30||(Co)variance, Correlation, Conditional / Iterated Expectation, Law of Total Variance||B-T 2||Lab 1
Lab 1 Sol
|2/4||Continuous Probability, Uniform, Exponential Distributions||B-T 3||HW 3
HW 3 Sol
|2/6||Gaussian Distribution, Derived Distributions, Continuous Bayes||B-T 3, 4.1-4.2||Lab 2
Lab 2 Sol
|2/11||Order Statistics, Convolution, Moment Generating Functions||B-T 4.3-4.6||HW 4|
|2/13||MGFs, Bounds/Concentration Inequalities (Markov, Chebyshev, Chernoff)||B-T 5.1||TBA|
|2/18||Convergence, Weak and Strong Law of Large Numbers, Central Limit Theorem||Convergence||TBA|
|2/20||No Lecture (Midterm 2/21)|
|2/25||Information Theory||Information Theory||TBA|
|2/27||Binary Erasure Channel Capacity||Capacity of BEC||TBA|
|3/3||Discrete Time Markov Chains, Calculations, Stationary Distribution||Markov Chains||TBA|
|3/5||DTMCs: Infinite States, Classification, Big Theorem||TBA|
|3/10||DTMCs: Reversibility, Hitting Time, First Step Equations||Reversibility||TBA|
|3/12||Poisson Processes: Counting Process, Memorylessness, Merging, Splitting||TBA|
|3/17||PP: Erlang Distribution, Random Incidence||TBA|
|3/19||Continuous Time Markov Chains: Rate Matrix and Stationary Distribution||TBA|
|3/31||CTMCs: Big Theorem, First Step Equations and Jump Chain||TBA|
|4/2||No Lecture (Midterm 4/3)|
|4/7||Erdos-Renyi Random Graphs||Random Graphs||TBA|
|4/9||Maximum Likelihood Estimation, Maximum a Posteriori Estimation||TBA|
|4/14||Statistical Hypothesis Testing, Neyman-Pearson Lemma||Hypothesis Testing||TBA|
|4/16||Minimum Mean Square Error Estimation, Vector Space of Random Variables||Hilbert Space||TBA|
|4/21||Linear Least Square Estimate||TBA|
|4/23||Jointly Guassian Random Variables||TBA|
|4/28||Orthogonal Updates and Kalman Filter||Geometric Derivation of Scalar Kalman Filter||TBA|
|4/30||Fisher Information and Cramer Rao Bound||TBA|