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

 

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        
1-2pm

Adarsh Muthuveeru-Subramaniam

Location: 3034 ECEB

   

 

 
2-3pm      

Jinyao Yang

Location: 5034 ECEB

3-4pm

Adi Pasic

Location: 3034 ECEB

   

Teja Gupta

Location: 5034 ECEB

4-5pm

Adarsh Muthuveeru-Subramaniam

Location: 3036 ECEB

 

Jinyao Yang

Location: 5034 ECEB

Dr. Mehrdad Moharrami

Location: 5034 ECEB

 

5-6pm  

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

 

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

Lecture Notes for Section C

Midterm 1:

Midterm 2:                                           

Final:

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

Course schedule (subject to change)
Homework #
Deadline
Lecture
dates
Concepts and assigned reading)[ Short videos] Recommended Study Problems
- - * 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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