EECS 126: Probability & Random Processes - Fall 2018

Announcements

Course Information

Lectures: Tuesday/Thursday, 11-12:30 PM, 105 Stanley

Staff

Instructors

NOTE:

TAs

Readers

Homework Party: Monday, 6-8 PM, 540AB Cory (DOP Center).

Lab Party: Thursday, 6-8 PM, 400 Cory

Textbooks

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

The lectures will not be recorded this semester.

Date Topics Reading Assignments
08/23 Introduction/Logistics, Probability Spaces/Axioms, Conditional Probability, Multiplication Rule, Law of Total Probability, Bayes Rule B-T 1 HW 1 (due 8/29), Lab 1 (due 8/31)
08/28 Bayes Rule, Independence, Conditional Independence, Discrete Random Variables B-T 1, B-T 2
08/30 Discrete R.V.s, Expectation, Linearity, St.Petersburg Paradox, Variance B-T 2 HW 1 Self-Grades (due 9/1), Lab 1 Self-Grades (due 9/1), HW 2 (due 9/5), Lab 2 (due 9/7)
09/04 Continuous Random Variables, Probability Density Function (PDF), Uniform Distribution, Exponential Distribution (Memoryless Property), Derived Distributions, Convolution B-T 3, B-T 4.1
09/06 Normal Distribution, Joint PDFs, Marginal PDFs, Conditional PDFs, Covariance B-T 3, B-T 4.1, B-T 4.2 HW 2 Self-Grades (due 9/10), HW 3 (due 9/12), Lab 2 Self Grades (due 9/10), Lab 3 (due 9/14)
09/11 Covariance, Correlation, Conditional Expectation, Iterated Expectation Diagnostic Quiz(Solutions) B-T 4.2, B-T4.3
09/13 Moment Generating Functions, Markov's Inequality, Chebyshev's Inequality, Chernoff Bounds B-T 4.4, B-T 4.5, B-T 5.1 HW3 Self Grades(due 9/17), HW4 (optional)
09/18 Chernoff Bounds, Convergence, Weak and Strong Law of Large Numbers, Central Limit Theorem B-T 5, W 2.1 - 2.3 Homework 5(due 9/27)
09/20 No Lecture
09/25 Convergence, Discrete Time Markov Chains (Markov Property, Transition Matrix) B-T 5, W 2.1-2.3 Modes of Convergence, B-T 7, W 1
09/27 Discrete Time Markov Chains (Recurrence,Transience, Stationary Distribution and Balance Equations, FSEs and Hitting Time B-T 7, W 1, 2.4, 2.6, 13.3 HW 5 Self Grades(due 10/1), Homework 6(due 10/3), Lab 4(due 10/5),
10/2 Discrete Time Markov Chains (Long Term Average, Positive Recurrence, Null Recurrence, Transience) B-T 7, W 1, 2.4, 2.6, 13.3, Markov Chains Note
10/4 Information Theory (Entropy, Source Coding, Typical Sets, Asymptotic Equipartition Property, Huffman Codes) Information Theory Homework 7 (due 10/10), HW 6 Self Grades(due 10/8), Lab 4 Self Grades (due 10/8), Lab 5 (due 10/12)
10/9 Information Theory (BSC, BEC, Channel Capacity) Capacity of the BEC
10/11 Erdos-Renyi Random Graphs Random Graphs Homework 8 (due 10/17), HW7 Self Grades (due 10/15), Lab 6 (due 10/19), Lab 5 Self Grades (due 10/15)
10/16 Random Graphs, Poisson Process Setup B-T 6.1 - 6.3, W 13.4
10/18 Poisson Processes, Merging, Splitting, Erlang Distribution B-T 6.1 - 6.3, W 13.4 Homework 9 (due 10/24), HW8 Self Grades (due 10/22), , Lab 6 Self Grades (due 10/22)
10/23 Erlang Distribution, Random Incidence Paradox, Continuous Time Markov Chains B-T 6.1 - 6.3, W 13.4, B-T 7.5, W 13.5
10/25 Continuous Time Markov Chains, Stationary Distribution, Hitting Time B-T 7.5, W 13.5 Homework 10, HW9 Self Grades
10/30 CTMCs-Embedded Markov Chain, MLE/MAP, Inference, Digital Communication B-T 7.5, W 13.5, B-T 8.1-8.2, 9.1, W 5.1
11/1 MAP Inference in Digital Communication W 5.1 - 5.4 Homework 11, HW 10 Self Grades(due 11/5)
11/6 Neyman-Pearson Hypothesis Testing W 5.5-5.6, B-T 9.3
11/8 Neyman Pearson Proof, Linear Least Squares Estimator, Vector Space of Random Variables W-6.5, W 7.1-7.3, B-T 8.4, Hilbert Space of Random Variables Homework 12(due 11/14)
11/13 LLSE, MMSE, Multivariate Gaussians W 7.1-7.5, W 6.3-6.4, B-T 8.4-8.5
11/15 MMSE, Jointly Gaussian R.V.s, Kalman Filter W 6.3-6.4,7.1-7.7, B-T 8.4-8.5, W-8.1 Homework 13 (due 11/21), HW12 Self Grades(due 11/19)
11/20 Lecture Cancelled
11/22 Thanksgiving
11/27 Kalman Filter W 7.6, 8.1-8.3, A Geometric Derivation of the Scalar Kalman Filter , Lecture Notes Homework 14 (due 12/4), HW13 Self Grades (due 12/3), Lab 7 (due 12/4)
11/29 Hidden Markov Models, Viterbi Algorithm W 9.1-9.2, Lecture Notes
12/4 Expectation Maximization W 9.3, Lecture Notes HW14 Self Grades (due 12/7), Lab 7 Self Grades (due 12/7)

Discussions

Discussion worksheets will be posted here.

Homework

Homework will be posted here.

Labs

Labs will be posted here.

Exams

Midterm 1

Location: VLSB 2050

Time: Wednesday, September 19, 8-10 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: Hearst Field Annex A1, Sunday, September 16, 2-4 PM.


Midterm 2

Location: Dwinelle 155

Time: Wednesday, November 7, 8-10 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: GPB 100, Sunday, November 4, 2-4 PM.


Final

Location: Stanley 105, Hearst Gym 234, Barrows 56 (please see piazza for details)

Time: Wednesday, December 12, 8-11 AM

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: Stanley 105, Thursday, December 6, 11AM -12.30 PM (during lecture)