ECE 313/MATH 362
PROBABILITY WITH ENGINEERING APPLICATIONS
Fall 2018 - Sections A, C, D, E and CSP
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.
Text :
ECE 313 Course Notes (hardcopy sold through ECE Stores,
pdf file available.) Corrections to notes.
Summary of office hours times and locations (starting on September 4th).
Hours
Monday
Tuesday
Wednesday
Thursday
Friday
Friday (CSP)
3 - 4pm
5034 ECEB
3034 ECEB
3034 ECEB
4034 ECEB
In Chicago
4 - 5pm
5034 ECEB
5 - 6pm
Section
Meeting time and place
Instructor
A
(CSP) 9 MWF
3081 ECE Building
Professor Venugopal Veeravalli
e-mail: vvv AT illinois dot edu
Office Hours: Fridays, 3-4:30pm, in Chicago
C 11 MWF
3017 ECE Building
Professor Zhizhen Zhao
e-mail: zhizhenz AT illinois dot edu
Office Hours: Wednesdays, 3-4pm, 3034 ECEB
D 1 MWF
3017 ECE Building
Professor Dimitrios Katselis
e-mail: katselis AT illinois dot edu
Office Hours: Fridays, 4-5pm, 4034 ECEB
E 2 MWF
3017 ECE Building
Professor Yi Lu
e-mail: yilu4 AT illinois dot edu
Office Hours: Mondays, 3-4pm, 5034 ECEB
Graduate Teaching Assistants
Hieu Tri Huynh
hthuynh2 AT illinois dot edu Office Hours:
Thursdays 4-6pm
Jason Nie
nie9 AT illinois dot edu Office Hours:
Mondays 4-5pm, Tuesdays 4-5pm
Vishesh Verma
vverma4 AT illinois dot edu Office Hours:
Tuesdays 5-6pm, Fridays 5-6pm
Lingda Wang
lingdaw2 AT illinois dot edu Office Hours:
Mondays 5-6pm
Ali Yekkehkhany
yekkehk2 AT illinois dot edu Office Hours:
Tuesdays 3-4pm, Fridays 3-4pm
Course schedule (subject to change) | |||
Checkpoint # Date |
Lecture dates |
Concepts (Reading)[ Short videos] | |
---|---|---|---|
1 Tue, 9/11 |
8/27-9/7 | * 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, 9/18 |
9/10-9/14 | * 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, 9/25 |
9/17-9/21 | * 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, 10/2 |
9/24-9/28 | * 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, 10/9 |
10/1-10/5 | * 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, 10/16 |
10/8-10/12 | * 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] * exponential distribution (Ch 3.4) [SAQ 3.4] |
|
7 Tue, 10/23 |
10/15-10/19 | * 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, 10/30 |
10/22-10/26 | * 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, 11/6 |
10/29-11/2 | * 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, 11/27 (skip 11/13) |
11/5-11/16 | * 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) |
|
11/19-11/23 | Thanksgiving vacation | ||
11 Tue, 12/4 |
11/26-11/30 | * 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, 12/11 |
12/3-12/7 | * 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] |
|
- | 12/11-12/13 | wrap up and review |
Optional Reading:
More Information
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