EECS 126: Probability & Random Processes

Announcements

Course Information

Lectures: Tuesday/Thursday, 2-3:30 PM, 100 Lewis

Staff

Instructors

TAs

Readers

Homework Party: Monday, 6:30-9 PM, 521 Cory.

Lab Party: Thursday, 6-8 PM, 212 Cory.

Textbooks

For more detailed information, please look at the full syllabus.

The lectures will not be recorded this semester.

Date Topics Reading Assignments
01/16 Introduction/Logistics, Probability Spaces/Axioms, Conditional Probability, Multiplication Rule, Law of Total Probability, Bayes Rule, Independence B-T 1 HW 1 (due 1/24)
01/18 Discrete Random Variables, Probability Mass Functions (PMFs), Joint Distributions, Expectation, Binomial Distribution, Poisson Distribution B-T 2 Lab 1 (due 1/26)
01/23 St. Petersburg Paradox, Conditioning of Random Variables, Independence of Random Variables, Variance, Covariance, Cumulative Distribution Function (CDF), Tail Sum Formula, Geometric Distribution (Memoryless Property), Coupon Collection B-T 2
01/25 Correlation, Poisson Properties, Entropy B-T 2 HW 1 Self-Grades (due 1/29), Lab 1 Self-Grades (due 1/29), HW 2 (due 1/31), Lab 2 (due 2/2)
01/30 Continuous Sample Spaces, Continuous Random Variables, Probability Density Function (PDF), Uniform Distribution, Exponential Distribution (Memoryless Property) B-T 3
02/01 Order Statistics, Gaussian Distribution, Derived Distributions, Convolution B-T 3, B-T 4.1-4.2 HW 2 Self-Grades (due 2/5), Lab 2 Self-Grades (due 2/5), HW 3 (due 2/7), Lab 3 (due 2/9)
02/06 Transforms, Moments, Moment Generating Functions (MGFs) B-T 4.3-4.6
02/08 Markov's Inequality, Chebyshev's Inequality, Chernoff Bound B-T 5.1, W 13.7 HW 3 Self-Grades (due 2/12), Lab 3 Self-Grades (due 2/12), HW 4 (due 2/12)
02/13 Weak Law of Large Numbers (WLLN), Convergence in Probability, Strong Law of Large Numbers (SLLN), Almost Sure Convergence, Central Limit Theorem (CLT) B-T 5.2-5.6, W 2.1-2.3, Modes of Convergence
02/15 No Lecture HW 4 Self-Grades (due 2/19), HW 5 (due 2/21), Lab 4 (due 3/2)
02/20 Digital Communication, Capacity of the Binary Erasure Channel (BEC)
02/22 Achievability of the BEC Capacity, Discrete-Time Markov Chains (DTMCs) Capacity of the BEC, W 1, 2.4, 13.3, B-T 7.1-7.4 HW 5 Self-Grades (due 2/26), HW 6 (due 2/28)
02/27 DTMCs: Irreducibility, Aperiodicity, Balance Equations, Big Theorem W 1, 2.4, 2.6, 13.3, B-T 7.1-7.4
03/01 DTMCs: PageRank, Classification, Infinite State Space, First-Step Equations (FSE) W 1, 2.4, 2.6, 13.3, B-T 7.1-7.4, Supplemental Notes HW 6 Self-Grades (due 3/5), Lab 4 Self-Grades (due 3/5), HW 7 (due 3/9), Lab 5 (due 3/16)
03/06 DTMCs: Absorption, Reversibility; Poisson Process: Construction Reversibility, B-T 6.1-6.3, W 13.4
03/08 Poisson Process: Memorylessness, Merging, Splitting B-T 6.1-6.3, W 13.4 HW 7 Self-Grades (due 3/12), HW 8 (due 3/14)
03/13 Poisson Process: Erlang Distribution, Random Incidence; Continuous-Time Markov Chains (CTMCs): Introduction, Markov Property, Rate Matrix B-T 7.5, W 13.5
03/15 CTMCs: Balance Equations, Big Theorem, FSE B-T 7.5, W 13.5 HW 8 Self-Grades (due 3/19), Lab 5 Self-Grades (due 3/19), HW 9 (due 3/19), Project (proposal due 4/4)
03/20 Random Graphs Random Graphs HW 9 Self-Grades (due 4/2)
03/22 Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) Estimation W 5.1, B-T 8.1-8.2, 9.1
03/27 Spring Break
03/29 Spring Break
04/03 Inference (Digital Communication) W 5.1-5.4, B-T 8.1-8.2, 9.1 Lab 6 (due 4/6), HW 10 (due 4/11)
04/05 Hypothesis Testing W 5.5-5.6, 6.5, B-T 9.3-9.4 Lab 6 Self-Grades (due 4/9), Project (due 4/18)
04/10 Linear Least Squares Estimation (LLSE), Vector Space of Random Variables W 7.1-7.5, B-T 8.3-8.5, Hilbert Space of Random Variables
04/12 Signal-to-Noise Ratio (SNR), Minimum Mean Square Error (MMSE) Estimation W 6.3-6.4, 8.1 HW 10 Self-Grades (due 4/16), HW 11 (due 4/20)
04/17 Joint Gaussians, Gram-Schmidt Process W 7.6-7.7, 8.1-8.2
04/19 Kalman Filter W 9.1-9.2, 9.4, A Geometric Derivation of the Scalar Kalman Filter, Lecture Notes HW 11 Self-Grades (due 4/23), Lab 7 (due 4/27), HW 12 (due 4/30)
04/24 Hidden Markov Models (HMMs), Viterbi Algorithm W 9.1-9.4, Lecture Notes
04/26 Clustering, Expectation Maximization (EM) Algorithm W 9.1-9.4, Lecture Notes HW 12 Self-Grades (due 5/4), Lab 7 Self-Grades (due 4/30)

Discussions

Discussion worksheets will be posted here.

Homework

Homework will be posted here.

Labs

Labs will be posted here.

Exams

Midterm 1

Location: 2050 VLSB

Time: Wednesday, February 14, 7-9 PM

Logistics: No electronics are allowed. No cheat sheets are allowed; we will provide a cheat sheet of our own. For DSP accommodations, please email the course staff.

Resources: Past Exams (Disclaimer: The content covered in the course has changed over the years, so older exams may not necessarily be representative of the material that will be covered on the midterm.)

Review Session: Wozniak Lounge, Saturday, February 10, 4-6 PM.

Midterm 2

Location: 2050 VLSB

Time: Wednesday, March 21, 7-9 PM

Logistics: No electronics are allowed. No cheat sheets are allowed; we will provide a cheat sheet of our own. For DSP accommodations, please email the course staff.

Resources: Past Exams (Disclaimer: The content covered in the course has changed over the years, so older exams may not necessarily be representative of the material that will be covered on the midterm.)

Review Session: 306 Soda, Friday, March 16, 6:30-8:30 PM.

Final

Location: TBA

Time: Monday, May 7, 11:30-2:30 PM

Logistics: No electronics are allowed. No cheat sheets are allowed; we will provide a cheat sheet of our own. For DSP accommodations, please email the course staff.

Resources: Past Exams (Disclaimer: The content covered in the course has changed over the years, so older exams may not necessarily be representative of the material that will be covered on the midterm.)

Review Session: 100 Lewis, Tuesday, May 1, 2-3:30 PM.