ECE 313/MATH 362
PROBABILITY WITH ENGINEERING APPLICATIONS
Fall 2017  Sections A,B,C,D and E
EE and CompE students must complete one of the two courses ECE 313 or Stat 410.
Prerequisite : Math 286 or Math 415
Exam times : See Exam information.
Inperson office hours  
Online office hours (through SOS on MasterProbo. See Homework) 
Hours  Monday  Tuesday  Wednesday  Thursday  Sunday 
34pm  5034 ECEB  5034 ECEB  
45pm  
78pm  Online  Online  Online  Online  Online 
88:30pm  
8:309pm 
Section  Meeting time and place  Instructor 

A  9 MWF 3015 ECE Building 
Professor Zhizhen Zhao
email: zhizhenz AT illinois dot edu Office Hours: Wednesdays, 45pm, 5034 ECEB 
B  10 MWF 3015 ECE Building 
Dimitrios Katselis email: katselis AT illinois dot edu Office Hours: Mondays, 45pm, 5034 ECEB 
C  11 MWF 3017 ECE Building 
Professor Lav R. Varshney
email: varshney AT illinois dot edu Office Hours: Wednesdays, 34pm, 5034 ECEB Slides and materials 
D  1 MWF 3017 ECE Building 
Xiaohan Kang
email: xiaohank AT illinois dot edu Office Hours: Mondays, 34pm, 5034 ECEB 
E  2 MWF 3017 ECE Building 
Professor Yi Lu email: yilu4 AT illinois dot edu Office Hours: Online Slides and materials 
Cheng Chen cchen130 AT illinois dot edu 
Office Hours: Online 
Chuchu Fan cfan10 AT illinois dot edu 
Office Hours: Online 
Du Su dusu3 AT illinois dot edu 
Office Hours: Online 
Vishesh Verma vverma4 AT illinois dot edu 
Office Hours: Online 
Ali Yekkehkhany yekkehk2 AT illinois dot edu 
Office Hours: Online 
Course schedule (subject to change)  
Checkpoint # Date 
Lecture dates 
Concepts (Reading)[ Short videos]  

1 Tue, 9/12 
8/289/8  * How to specify a set of outcomes, events, and probabilities for a given experiment (Ch 1.2) * set theory (e.g. de Morgan's law, Karnaugh maps for two sets) (Ch 1.2) * using principles of counting and over counting; binomial coefficients (Ch 1.31.4) [ILLINI, SAQ 1.3, SAQ 1.4, PokerIntro, PokerFH2P] * using Karnaugh maps for three sets (Ch 1.4) [Karnaughpuzzle, SAQ1.2] 

2 Tue, 9/19 
9/119/15  * random variables, probability mass functions, and mean of a function of a random variable (LOTUS) (Ch 2.1, first two pages of Ch 2.2) [pmfmean] * scaling of expectation, variance, and standard deviation (Ch 2.2) [SAQ 2.2] * conditional probability (Ch 2.3) [team selection] [SAQ 2.3] * independence of events and random variables (Ch 2.4.12.4.2) [SimdocIntro] [SimdocMinhash1] 

3 Tue, 9/26 
9/189/22  * binomial distribution (how it arises, mean, variance, mode) (Ch 2.4.32.4.4) [SAQ 2.4] [bestofseven] * geometric distribution (how it arises, mean, variance, memoryless property) (Ch. 2.5) [SAQ 2.5] * Bernoulli process (definition, connection to binomial and geometric distributions) (Ch 2.6) [SAQ 2.6] * Poisson distribution (how it arises, mean, variance) (Ch 2.7) [SAQ 2.7] 

4 Tue, 10/3 
9/259/29  * Maximum likelihood parameter estimation (definition, how to calculate for continuous and discrete parameters) (Ch 2.8) [SAQ 2.8] * Markov and Chebychev inequalities (Ch 2.9) * confidence intervals (definitions, meaning of confidence level) (Ch 2.9) [SAQ 2.9,SimdocMinhash2] * law of total probability (Ch 2.10) [deuce] [SAQ 2.10] * Bayes formula (Ch. 2.10) 

5 Tue, 10/10 
10/210/6  * Hypothesis testing  probability of false alarm and probability of miss (Ch. 2.11) * ML decision rule and likelihood ratio tests (Ch 2.11) [SAQ 2.11] * MAP decision rules (Ch 2.11) * union bound and its application (Ch 2.12.1) [SAQ 2.12] * network outage probability and distribution of capacity, and more applications of the union bound (Ch 2.12.22.12.4) 

6 Tue, 10/17 
10/910/13  * cumulative distribution functions (Ch 3.1) [SAQ 3.1] * probability density functions (Ch 3.2) [SAQ 3.2] [simplepdf] * uniform distribution (Ch 3.3) [SAQ 3.3] * exponential distribution (Ch 3.4) [SAQ 3.4] 

7 Tue, 10/24 
10/1610/20  * Poisson processes (Ch 3.5) [SAQ 3.5] * Erlang distribution (Ch 3.5.3) * scaling rule for pdfs (Ch. 3.6.1) [SAQ 3.6] * Gaussian (normal) distribution (e.g. using Q and Phi functions) (Ch. 3.6.2) [SAQ 3.6] [matlab help including Qfunction.m] 

8 Tue, 10/31 
10/2310/27  * the central limit theorem and Gaussian approximation (Ch. 3.6.3) [SAQ 3.6] * ML parameter estimation for continuous type random variables (Ch. 3.7) [SAQ 3.7] * the distribution of a function of a random variable (Ch 3.8.1) [SAQ 3.8] * generating random variables with a specified distribution (Ch 3.8.2) * failure rate functions (Ch 3.9) [SAQ 3.9] * binary hypothesis testing for continuous type random variables (Ch 3.10) [SAQ 3.10] 

9 Tue, 11/7 
10/3011/3  * joint CDFs (Ch 4.1) [SAQ 4.1] * joint pmfs (Ch 4.2) [SAQ 4.2] * joint pdfs (Ch 4.3) [SAQ 4.3] 

10 Tue, 11/28 (skip 11/14) 
11/611/17  * joint pdfs of independent random variables (Ch 4.4) [SAQ 4.4] * distribution of sums of random variables (Ch 4.5) [SAQ 4.5] * more problems involving joint densities (Ch 4.6) [SAQ 4.6] * joint pdfs of functions of random variables (Ch 4.7) [SAQ 4.7] (Section 4.7.2 and 4.7.3 will not be tested in the exams) 

11/2011/24  Thanksgiving vacation  
11 Tue, 12/5 
11/2712/1  * correlation and covariance: scaling properties and covariances of sums (Ch 4.8) [SAQ 4.8] * sample mean and variance of a data set, unbiased estimators (Ch 4.8, Example 4.8.7) * minimum mean square error unconstrained estimators (Ch 4.9.2) * minimum mean square error linear estimator (Ch 4.9.3) [SAQ 4.9] 

12 Tue, 12/12 
12/412/8  * law of large numbers (Ch 4.10.1) * central limit theorem (Ch 4.10.2) [SAQ 4.10] * joint Gaussian distribution (Ch 4.11) (e.g. five dimensional characterizations) [SAQ 4.11] 

  12/1112/13  wrap up and review 
Optional Reading:
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