ECE 313/MATH 362
PROBABILITY WITH ENGINEERING APPLICATIONS
Spring 2018 - Sections B,C,D,F and G
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 January 22).
In-person office hours
Online office hours (through SOS on MasterProbo. See Homework) SOS requests are answered throughout the day, but response will be almost real-time durring online office hours.
Hours
Monday
Tuesday
Wednesday
Thursday
Friday
3-4pm
5034 ECEB
5034 ECEB
4032 ECEB
4-5pm
2036 ECEB
5-6pm
4036 ECEB
7-8pm
Online
Online
8-9pm
Section
Meeting time and place
Instructor
C 10 MWF
3017 ECE Building
Professor Olgica Milenkovic
e-mail: milenkov AT illinois dot edu
Office Hours: Wednesdays, 3-4pm, 5034 ECEB
G 11 MWF
3015 ECE Building
Professor Juan Alvarez
e-mail: alvarez AT illinois dot edu
Office Hours: Mondays, 3-4pm, 5034 ECEB
review notes, figures and handouts
D 11 MWF
3017 ECE Building
Professor Alejandro Dominguez-Garcia
e-mail: aledan AT illinois dot edu
Office Hours: Thursdays, 3-4pm, 4032 ECEB
F 1 MWF
3017 ECE Building
Professor Idoia Ochoa
e-mail: idoia AT illinois dot edu
Office Hours: Wednesdays, 4-5pm, 5034 ECEB
B 2 MWF
3017 ECE Building
Professor Yi Lu
e-mail: yilu4 AT illinois dot edu
Office Hours: Online
Slides and materials
Graduate Teaching Assistants
Cheng Chen
cchen130 AT illinois dot edu Office Hours:
Online
Ge Yu
geyu3 AT illinois dot edu Office Hours:
Online
Du Su
dusu3 AT illinois dot edu Office Hours:
Online
Vishesh Verma
vverma4 AT illinois dot edu Office Hours:
Tuesdays 4-6pm, Fridays 5-6pm
Ali Yekkehkhany
yekkehk2 AT illinois dot edu Office Hours:
Online
Course schedule (subject to change) | |||
Checkpoint # Date |
Lecture dates |
Concepts (Reading)[ Short videos] | |
---|---|---|---|
1 Tue, 1/30 |
1/17-1/26 | * 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] |
|
2 Tue, 2/6 |
1/29-2/2 | * 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] |
|
3 Tue, 2/13 |
2/5-2/9 | * 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] |
|
4 Tue, 2/20 |
2/12-2/16 | * 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) |
|
5 Tue, 2/27 |
2/19-2/23 | * 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) * 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, 3/6 |
2/26-3/2 | * 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, 3/13 No Lecture 3/9, EOH |
3/5-3/7 | * 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, 3/27 |
3/12-3/16 | * 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] |
|
3/19-3/23 | Spring vacation | ||
9 Tue, 4/3 |
3/26-3/30 | * 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, 4/17 (skip 4/10) |
4/2-4/13 | * 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 Thu, 4/26 |
4/16-4/20 | * 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, 5/1 |
4/23-4/27 | * 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] |
|
- | 4/30-5/2 | wrap up and review |
Optional Reading:
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