Section	Meeting time and place	Instructor
C	10 MWF 3017 ECE Building	Professor Naresh Shanbhag e-mail: shanbhag AT illinois dot edu Office Hours: Wednesdays 3:30-4:30pm, 414 CSL
D	11 MWF 3017 ECE Building	Professor Naresh Shanbhag e-mail: shanbhag AT illinois dot edu Office Hours: Wednesdays 3:30-4:30pm, 414 CSL
F	1 MWF 3017 ECE Building	Professor Aiguo Han e-mail: han51 AT illinois dot edu Office Hours: Mondays 4-5pm, 5034 ECEB
B	2 MWF 3017 ECE Building	Professor Yi Lu e-mail: yilu4 AT illinois dot edu Office Hours: Wednesdays 3-4pm, 4036 ECEB

Graduate Teaching Assistants

Hieu Tri Huynh hthuynh2 AT illinois dot edu	Office Hours: Tuesdays and Thursdays, 5-6pm
Du Su dusu3 AT illinois dot edu	Office Hours: Fridays 3-5pm
Liming Wang lwang114 AT illinois dot edu	Office Hours: Tuesdays 5-6 pm
Lingda Wang lingdaw2 AT illinois dot edu	Office Hours: Mondays and Fridays, 5-6pm
Ali Yekkehkhany yekkehk2 AT illinois dot edu	Office Hours: Tuesdays 3-5pm

Concept constellation

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Course schedule (subject to change)
Checkpoint # Date	Lecture dates	Concepts (Reading)[ Short videos]
1 Tue, 1/29	1/14-1/25	* 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 1.3-1.4) [ILLINI, SAQ 1.3, SAQ 1.4, PokerIntro, PokerFH2P] * using Karnaugh maps for three sets (Ch 1.4) [Karnaughpuzzle, SAQ1.2]
2 Tue, 2/5	1/28-2/1	* 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 2.4.1-2.4.2) [SimdocIntro] [Simdoc-Minhash1]
3 Tue, 2/12	2/4-2/8	* binomial distribution (how it arises, mean, variance, mode) (Ch 2.4.3-2.4.4) [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, 2/19	2/11-2/15	* 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,Simdoc-Minhash2] * law of total probability (Ch 2.10) [deuce] [SAQ 2.10] * Bayes formula (Ch. 2.10)
5 Tue, 2/26	2/18-2/22	* 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 2.12.2-2.12.4)
6 Tue, 3/5	2/25-3/1	* 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, 3/12 No Lecture 3/8, EOH	3/4-3/6	* 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, 3/26	3/11-3/15	* 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]
	3/18-3/22	Spring vacation
9 Tue, 4/2	3/25-3/39	* 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, 4/16 (skip 4/9)	4/1-4/12	* 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 Tue, 4/23	4/15-4/19	* 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, 4/30	4/22-4/26	* 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]
-	4/29-5/1	wrap up and review

Optional Reading:

• D. V. Sarwate, Probability with Engineering Applications, Powerpoint Slides for ECE 313, Fall 2000