ECE 313 Fall 2022, University of Illinois at Urbana-Champaign

ECE 313/MATH 362

PROBABILITY WITH ENGINEERING APPLICATIONS

Fall 2022

ECE 313 (also cross-listed as MATH 362) is an undergraduate course on probability theory and statistics with applications to engineering problems primarily chosen from the areas of communications, control, signal processing, and computer engineering. Students taking ECE 313 might consider taking ECE 314, Probability Lab, at the same time.

EE and CompE students must complete one of the two courses ECE 313 or Stat 410.

Prerequisite : Math 257 or Math 416

Exam times : See Exam information.

Text : ECE 313 Course Notes (hardcopy sold through ECE Stores, pdf file available.)

Times for office hours (beginning Aug. 24).

Adi's tutorial session would start from Sep. 1

Hours	Monday	Tuesday	Wednesday	Thursday	Friday
9-11am	Teja Gupta Location: 3036 ECEB
11am-12pm	Haneul (Chloe) Kim Location: 3034 ECEB			Professor Dimitrios Katselis Location: 3032 ECEB
12pm-1pm	Haneul (Chloe) Kim Location: 3034 ECEB
1-2pm	Adarsh Muthuveeru-Subramaniam Location: 3034 ECEB
2-3pm	Adarsh Muthuveeru-Subramaniam Location: 3034 ECEB				Jinyao Yang Location: 5034 ECEB
3-4pm	Adi Pasic Location: 3034 ECEB			Teja Gupta Location: 5034 ECEB	Jinyao Yang Location: 5034 ECEB
4-5pm	Adarsh Muthuveeru-Subramaniam Location: 3036 ECEB		Jinyao Yang Location: 5034 ECEB	Dr. Mehrdad Moharrami Location: 5034 ECEB
5-6pm	Adarsh Muthuveeru-Subramaniam Location: 3036 ECEB		Jinyao Yang Location: 5034 ECEB	Adi Pasic(Tutorial Session) Location: 2017 ECEB (Starts 09/01)	Prof. Zhizhen Zhao Location: Zoom
6-7pm

8:00-9:00pm	Haneul (Chloe) Kim Location: 3034 ECEB

Meeting Details

Section	Instructor	Lecture Meeting Time & Place	Office Hour Meeting Time & Place
A	Professor Zhizhen Zhao e-mail: zhizhenz AT illinois dot edu	10:00 AM - 10:50 AM MWF Online Lecture: Zoom Lecture Notes	Office Hours: Friday 5pm - 6pm Location: Zoom
C	Dr. Mehrdad Moharrami email: moharami AT illinois dot edu	11:00 AM - 11:50 AM MWF Lecture Location: 3017 ECEB	Office Hours: Thursday 4pm - 5pm Location: 5034 ECEB
D	Professor Dimitrios Katselis e-mail: katselis AT illinois dot edu	1:00 PM - 1:50 PM MWF Lecture Location: 3017 ECEB	Office Hours: Thursday 11am - 12pm Location: 3032 ECEB

Section

Instructor

Lecture Meeting Time & Place

Office Hour Meeting Time & Place

Professor Zhizhen Zhao
e-mail: zhizhenz AT illinois dot edu

10:00 AM - 10:50 AM MWF
Online Lecture: Zoom

Lecture Notes

Office Hours: Friday 5pm - 6pm

Location: Zoom

Dr. Mehrdad Moharrami
email: moharami AT illinois dot edu

11:00 AM - 11:50 AM MWF
Lecture Location: 3017 ECEB

Office Hours: Thursday 4pm - 5pm

Location: 5034 ECEB

Professor Dimitrios Katselis
e-mail: katselis AT illinois dot edu

1:00 PM - 1:50 PM MWF
Lecture Location: 3017 ECEB

Office Hours: Thursday 11am - 12pm

Location: 3032 ECEB

Lecture recordings
Mediaspace: for long-term storage purpose, update after each lecture

Lecture Notes for Section C

Midterm 1:

08-22 Lecture 1 (Week 1)
08-24 Lecture 2 (Week 1)
08-26 Lecture 3 (Week 1)
08-29 Lecture 4 (Week 2)
08-31 Lecture 5 (Week 2)
09-02 Lecture 6 (Week 2)
09-07 Lecture 7 (Week 3)
09-09 Lecture 8 (Week 3)
09-12 Lecture 9 (Week 4)
09-14 Lecture 10 (Week 4)
09-16 Lecture 11 (Week 4)
09-19 Lecture 12 (Week 5)
09-21 Lecture 13 (Week 5)
09-23 Lecture 14 (Week 5)
10-03 Lecture 18 (Review Lecture)

Midterm 2:

09-26 Lecture 15 (Week 6)
09-28 Lecture 16 (Week 6)
09-30 Lecture 17 (Week 6)
10-05 Lecture 19 (Week 7)
10-07 Lecture 20 (Week 7)
10-10 Lecture 21 (Week 8)
10-12 Lecture 22 (Week 8)
10-14 Lecture 23 (Week 8)
10-19 Lecture 24 (Week 9)
10-21 Lecture 25 (Week 9)
10-24 Lecture 26 (Week 10)
10-26 Lecture 27 (Week 10)
10-28 Lecture 28 (Week 10)
11-04 Lecture 31 (Review Lecture)

Final:

10-31 Lecture 29 (Week 11)
11-02 Lecture 30 (Week 11)
11-09 Lecture 32 (Week 12)
11-11 Lecture 33 (Week 12)
11-14 Lecture 34 (Week 13)
11-16 Lecture 35 (Week 13)
11-18 Lecture 36 (Week 13)
11-28 Lecture 37 (Week 14)
11-30 Lecture 38 (Week 14)
12-02 Lecture 39 (Week 14)
12-05 Lecture 40 (Week 15)
12-07 Lecture 41 (Review Lecture)

Format: Time【Location】

Teja Gupta tejag2 AT illinois dot edu	Office Hours: Monday 9am - 11am 【3036 ECEB】; Thursday 3pm - 4pm【5034 ECEB】
Haneul (Chloe) Kim kim705 AT illinois dot edu	Office Hours: Monday 11am - 1pm【3034 ECEB】; Monday 7:30pm - 8:30pm【3034 ECEB】
Jinyao Yang jinyaoy2 AT illinois dot edu	Office Hours: Wednesday 4pm - 6pm【5034 ECEB】; Friday 2pm - 4pm【5034 ECEB】
Adi Pasic pasic2 AT illinois dot edu	Office Hours: Monday 3pm - 4pm【3034 ECEB】; Tutorial Session: Thursday 5pm - 7pm 【2017 ECEB】
Adarsh Muthuveeru-Subramaniam adarshm2 AT illinois dot edu	Office Hours: Monday 1pm - 3pm【3034 ECEB】; Monday 4pm - 6pm【3036 ECEB】
Yuechen Wang yuechen6 AT illinois dot edu	Office Hours:

Concept constellation

Homework # Deadline	Lecture dates	Concepts and assigned reading)[ Short videos]	Recommended Study Problems
Course schedule (subject to change)
-	-	* the sum of a geometric series and power series for exp(x) * basic calculus: the chain rule for differentiation and use of logarithms	-
1 Mon 8.29 11:59:00pm for all HW deadlines below	8.22-8.26	* 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]	SAQs, i.e. Solution Available Question, (on p. 20) for Sections 1.2, 1.3, 1.4. Problems (pp. 21-24) 1.2, 1.4, 1.6, 1.8, 1.10, 1.12. Optional: [SAQ 1.5]
2 Tues 9.6	8.29-9.2	* 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]	SAQs (pp. 74-75) for Sections 2.2-2.4 Problems (pp. 77-82) 2.2, 2.4, 2.6, 2.8, 2.10, 2.12, 2.16.
3 Mon 9.12	9.5-9.9	* 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]	SAQs (p. 75) for Sections 2.4-2.7 Problems (pp. 81-84) 2.14, 2.18, 2.20, 2.22, 2.24
4 Mon 9.19	9.12-9.16	* 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)	SAQs (pp. 75-76) for Sections 2.8-2.10 Problems (pp. 85-88) 2.26, 2.28, 2.30, 2.32, 2.34
5 Tue 9.27	9.19-9.23	* 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)	SAQs (p. 76) for Sections 2.11 & 2.12 Problems (pp. 88-93) 2.36, 2.38, 2.40, 2.42, 2.44, 2.46
6 Mon 10.3	9.26-9.30	* union bound and its application (Ch 2.12.1) [SAQ 2.12] * 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]	SAQs (p. 146-147) for Sections 3.1-3.4. Problems (pp.149-151) 3.2, 3.4, 3.6, 3.8, 3.10.
7 Mon 10.10	10.3-10.7	* exponential distribution (Ch 3.4) [SAQ 3.4] * 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]	SAQs (p 147) for Sections 3.5 & 3.6 . Problems (p. 152-154) 3.12, 3.14, 3.16, 3.18, 3.20
8 Mon 10.17	10.10-10.14	* Gaussian (normal) distribution (e.g. using Q and Phi functions) (Ch. 3.6.2) [SAQ 3.6] [matlab help including Qfunction.m] * 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]	SAQs (pp. 147-148) for Sections 3.7-3.10. Problems (pp. 154-159) 3.22, 3.24, 3.26, 3.28, 3.30, 3.32, 3.34, 3.38
9 Mon 10.24	10.17-10.21	* 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) * binary hypothesis testing for continuous type random variables (Ch 3.10) [SAQ 3.10]	SAQs (pp. 147-148) for Sections 3.7-3.10. Problems (pp. 154-159) 3.22, 3.24, 3.26, 3.28, 3.30, 3.32, 3.34, 3.38
10 Mon 10.31	10.24-10.28	* joint CDFs (Ch 4.1) [SAQ 4.1] * joint pmfs (Ch 4.2) [SAQ 4.2] * joint pdfs (Ch 4.3) [SAQ 4.3]	SAQs (pp. 223-224) for Sections 4.1-4.3. Problems (pp. 226-228) 4.2, 4.6, 4.10.
11 Mon 11.14	10.31-11.11	* 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]	SAQs (p. 224) for Sections 4.4-4.7. Problems (p. 226-230) 4.4, 4.8, 4.12, 4.14, 4.16.
12 Mon 11.28	11.14-11.18	* 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) * 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)	SAQs (p. 224) for Sections 4.8-4.9. Problems (p. 230-233) 4.18, 4.20, 4.22, 4.24, 4.26, 4.28
	11.21-11.25	Thanksgiving vacation
13 Mon 12.5	11.28-12.2	* minimum mean square error unconstrained estimators (Ch 4.9.2) * minimum mean square error linear estimator (Ch 4.9.3) [SAQ 4.9] * 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]	SAQs (p.225) for Sections 4.10-4.11 Problems (pp.233-237) 4.30, 4.32, 4.34, 4.36, 4.38, 4.40, 4.42.
-	12.5-12.7	wrap up and review

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

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Concept constellation

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Lecture recordings
Mediaspace: for long-term storage purpose, update after each lecture