Image ECE ILLINOIS

 

ECE 313/MATH 362

PROBABILITY WITH ENGINEERING APPLICATIONS

Spring 2026

Link to Section A (Professor Iyer Section)

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.

Homeworks : Homeworks will be issued weekly on Fridays and will be due the next Friday at 7pm, through Gradescope. On each homework, we will randomly select about half the problems to be fully graded. Homework assignments and solutions will be posted here. Typesetting with LaTeX is allowed, however, no additional credit will be awarded to typeset homework.

Campuswire: We encourage you to use Campuswire for online discussions related to the class. The course staff will try to answer any question within 48 hours during weekdays. Online questions and emails will not be answered during weekends. Questions during the weekends before exams can be answered during regular office hours. Self-enrollment code for Campuswire is 1401.

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


Office Hour Schedule (TA Office hours start from the second week of the semester) 

Before attending office hours, students are strongly encouraged to read the homework problems and lecture material and formulate their questions for the instructors/TAs. This will facilitate a more productive discussion and allow more students to get detailed feedback.

Hours Monday Tuesday Wednesday Thursday Friday
9 am-10 am Christopher [2036 ECEB] Xu Chen [5040 ECEB] Christopher [3034 ECEB]   Lily [4036 ECEB] Yu-Han [4034 ECEB]  
10 am-11 am   Yu-Lin [3034 ECEB]  
11 am-12 pm          
12 pm-1 pm          
1 pm-2 pm          
2 pm-2:30 pm Shilan [3034 ECEB]        
2:30 pm-3 pm   Dimitrios [3013 ECEB]    
3 pm-3:30 pm Vignesh [3034 ECEB] Dimitrios [3013 ECEB] Vignesh [2036 ECEB]    
3:30 pm-4 pm      
4 pm-5 pm Shilan [3034 ECEB] Yun-Han[3003 ECEB]   Lily [4036 ECEB]  
5 pm-6 pm   Jiawei [3013 ECEB] Lily [4036 ECEB] Yun-Han [3034 ECEB]  
6 pm-7 pm        

Meeting Details

Section Meeting time and place Instructor

B

11:00 AM - 11:50 AM MWF
3017 ECEB
Professor Xu Chen
e-mail: xuchen1 AT illinois dot edu
Office Hours:  Monday 9:00 AM - 10:00 AM, 
5040 ECEB

C

1:00 PM - 1:50 PM MWF
1013 ECEB
Professor Dimitrios Katselis
e-mail: katselis AT illinois dot edu
Office Hours: Wednesday 2:30-3:30 PM, 3013 ECEB

G

11:00 AM - 12:20 AM TR
1302 Everitt Lab
Professor Yu-Lin Wei
e-mail: yulinlw2 AT illinois dot edu
Office Hours:  Wednesday 10:00 AM - 11:00 AM, 3034 ECEB

 

Graduate Teaching Assistants

 

Name Office Hour Time Office Hour Location
Yun-Han Li
yunhanl2 AT illinois dot edu
Monday 4-6 PM 3003 ECEB
Thursday 5-7 PM 3034 ECEB
Shilan He
shilanh2 AT illinois dot edu
Monday 2-6 PM 3034 ECEB
Yu-Han Huang
yuhanhh2 AT illinois dot edu
Thursday 9-11 AM 4034 ECEB
Christopher Lee
csl9 AT illinois dot edu
Monday 9-11 AM 2036 ECEB
Tuesday 9-11 AM 3034 ECEB
Jiawei Li
jiaweil9 AT illinois dot edu
Wednesday 5-7 PM 3013 ECEB
Phoo Pyae Pyae Linn (Lily)
plinn2 AT illinois dot edu
Thursday 9-11 AM 4036 ECEB
Thursday 4-6 PM 4036 ECEB
Vignesh Sundaresha
vs49 AT illinois dot edu
Tuesday 3-5 PM 3034 ECEB
Wednesday 3-5 PM 2036 ECEB

Concept constellation

 

Lecture Dates Written Homework #
Deadline
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

SAQs - Solution Available Questions

Problems at the end of each chapter

Examples within each chapter

Week of Jan 20

0

HW 0 will not be graded

7:00:00pm for all HW deadlines below

* 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) (Ch 1.2)

[YL L1 Pre] [YL L1 Post] [YL L2 Pre] [YL L2 Post]

SAQs, (on p. 20) for Sections 1.2

Problems (p. 21-22) 1.2, 1.4, 1.6b, 1.8

Week of Jan 26

1

* using principles of counting and over counting; binomial coefficients (Ch 1.3-1.4) [ILLINI,  SAQ 1.3, SAQ 1.4, PokerIntro, PokerFH2P]

* 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]

[YL L3 Pre] [YL L3 Post] [YL L4 Pre] [YL L4 Post]

SAQs (p. 20, 74) for Sections 1.3-1.4, 2.2

Problems (p. 23-24, 77-80) 1.10, 1.12, 2.2, 2.4, 2.6, 2.8, 2.10

Optional: [SAQ 1.5]

Week of Feb 2

2

* conditional probability (Ch 2.3) [team selection] [SAQ 2.3]

*law of total probability (Ch 2.10) [deuce] [SAQ 2.10]

*Bayes formula (Ch. 2.10)

* independence of events and random variables (Ch 2.4.1-2.4.2) [SimdocIntro] [Simdoc-Minhash1]

SAQs (p. 74-76) for Sections 2.3, 2.10

Problems (p. 81-82, 86-87) , 2.12, 2.14, 2.16, 2.32, 2.34

Week of Feb 9

3

* 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. 74-75) for Section 2.4-2.7

Problems (p. 82-84) 2.18, 2.20, 2.22, 2.24

Week of Feb 16

4

* Markov and Chebychev inequalities (Ch 2.9)

* confidence intervals (definitions, meaning of confidence level) (Ch 2.9) [SAQ 2.9,Simdoc-Minhash2]

* 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]

SAQs (p. 75-76) for Sections 2.8, 2.9, 2.11

Problems (p. 85-86, 88-90) 2.26, 2.28, 2.30, 2.36a-b, 2.38, 2.40a

Week of Feb 23

5

* MAP decision rules (Ch 2.11)

* 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]

SAQs (p. 76, 146) for Sections 2.11, 2.12, 3.1-3.2

Problems (p. 88-92, 149-151) 2.36, 2.40, 2.42, 2.44, 2.46, 3.2, 3.4, 3.6, 3.8

Week of March 2

6

* uniform distribution (Ch 3.3) [SAQ 3.3]

* exponential distribution (Ch 3.4) [SAQ 3.4]

* Poisson processes (Ch 3.5) [SAQ 3.5]

* Erlang distribution (Ch 3.5.3)

SAQs (p 147) for Sections 3.3-3.5

Problems (p. 151-152) 3.10, 3.12, 3.14

Week of March 9

7

* scaling rule for pdfs (Ch. 3.6.1)

* 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]

SAQs (p. 147) for Sections  3.6

Problems (p. 152-154) 3.16, 3.18, 3.20

Spring Break

Week of March 23

8

* 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)

SAQs (pp. 147) for Sections 3.7, 3.8

Problems (pp. 154-159) , 3.22, 3.24, 3.26, 3.28, 3.30, 3.32, 3.34a, 3.38a-b,d

Week of March 30

9

* binary hypothesis testing for continuous type random variables (Ch 3.10) [SAQ 3.10]

* joint CDFs (Ch 4.1) [SAQ 4.1]

* joint pmfs (Ch 4.2) [SAQ 4.2]

 

SAQs (p. 148, 223) for Sections 3.10, 4.1-4.2

Problems (p. 226) 4.2

Week of April 6

10

* joint pdfs (Ch 4.3) [SAQ 4.3]

* joint pdfs of independent random variables (Ch 4.4) [SAQ 4.4]

* distribution of sums of random variables (Ch 4.5) [SAQ 4.5]

SAQs (p. 223-224) for Sections 4.3-4.5

Problems (p. 226-229) 4.4, 4.6, 4.8, 4.10, 4.12, 4.14

Week of April 13

11

* 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)

* correlation and covariance: scaling properties and covariances of sums (Ch 4.8) [SAQ 4.8]

SAQs (p. 224) for Sections 4.6-4.8

Problems (p. 229-231) 4.16, 4.18, 4.20, 4.22

Week of April 20

12

* 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]

SAQs (p.225) for Sections 4.9

Problems (p. 231-233) 4.24, 4.26, 4.28

Week of April 27

13

* 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 (p. 233-237) 4.30, 4.32, 4.34, 4.36, 4.38, 4.40, 4.42

Week of May 4 -

wrap up and review

-

More Information

 

Grading Policies

Written Homework

Exams

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