Week
|
Monday
|
Wednesday
|
Friday
|
1
|
Aug 24: Course overview,
motivation (Ch. 1)
|
Aug 26: Sample spaces and
events, sets, identities (Ch. 2.1, 2.2)
|
Aug 28: Inclusion-exclusion
principle (2.2), random variables (Ch
2.3)
|
2
|
Aug 31: HW
1 due, combinatorics
(Ch. 2.4)
|
Sep 2: Combinatorics (2.4)
|
Sep 4: Definition of probability
(Ch 3.1), basic properties (3.2) |
3
|
Sep
7: Labor Day
|
Sep 9: Distributions of random
variables, probability mass functions
(3.3) |
Sep
11: HW 2 due, Quiz 1, probability density
functions (3.3)
|
4
|
Sep 14: Conditional probability,
chain rule for probabilities (4.1)total
probability theorem, Bayes rule (4.1) |
Sep 16: Total
probability theorem, Bayes rule (4.1) |
Sep 18: HW 3
due, Independence (4.2),
conditional independence
|
5
|
Sep 21: Independence (4.2),
conditional independence |
Sep 23: Expected value
of a
r.v.(5.1), variance standard deviation of a r.v. (5.1)
|
Sep 25: HW 4
due, expected value, moments, moment generating functions (5.1),
Chebyshev's inequality (5.2) |
6
|
Sep 28: Moment generating
functions (5.1), quantiles, median, mode (5.3)
|
Sep 30: Bernoulli trials,
binomial distribution (6.1.1) |
Oct 2: HW 5
due, binomial distribution (6.1.1), midterm information and brief
review |
7
|
Oct
5: Midterm 1, Chapters 1-5, up to
(including) Sep 28 lecture
|
Oct 7: Midterm overview
|
Oct 9: Geometric distribution
(6.1.2), negative binomial
distribution (handout)
|
8
|
Oct
12: Fall Break
|
Oct 14: Poisson distribution
(6.1.3), hypergeometric
dsitribution (6.1.4)
|
Oct
16: HW 6 due, Quiz 2, Poisson distribution
(6.1.3)
|
9
|
Oct 19: Hypergeometric
distribution (6.1.4), uniform distribution
(6.2.5)
|
Oct 21: Normal approximation to
binomial distribution (handout), Gaussian distribution
(6.2.4) (presentation: Wyatt Clarke)
|
Oct 23: Gaussian distribution
(6.2.4) |
10
|
Oct 26: HW 7
due, exponential
distribution (6.2.2) (presentation: Ure
Ganbold)
|
Oct 28: Gamma
distribution (6.2.1) Transformation of a
single random variable (11.1), log-normal distribution, Chi-square
distribution (6.2.3) (presentation: Kicho Yu; topic: Carl Friedrich
Gauss)
|
Oct 30: HW 8
due, jointly
distributed
random variables (7.1) |
11
|
Nov 2: Marginal and
conditional distributions and densities (7.2) |
Nov 4: Marginal and
conditional distributions and densities (7.2), independent random
variables (10.1) |
Nov
6: Quiz 3, independent random variables (10.1) |
12
|
Nov 9: HW 9
due, sums of independent random variables (10.2) |
Nov 11: Sums of
independent
random variables, convolution (10.2), joint moment generating
functions (8.1) |
Nov 13: Transformation of
several
random variables (11.2-11.3), midterm information and brief
review
|
13
|
Nov
16: Midterm 2, 8:00-9:15pm, B155
Lawson Building (regular lecture is replaced by question/answer
session)
|
Nov 18: HW 10
due, midterm overview,
expectation of a sum of random variables
|
Nov 20: Expectation of a sum of
random variables (presentation: Jiayin Wang)
|
14
|
Nov 23: Variance of a sum of
random variables, covariance (8.2) (presentation: Emily Raymond)
|
Nov
25: Thanksgiving Break
|
Nov
27: Thanksgiving Break
|
15
|
Nov 30: Covariance, correlation
(8.2), bivariate normal distribution (9.3) (presentation: Mike Hunter)
|
Dec 2: HW 11
due, conditional
expectation,
conditional variance (7.2) (presentation: Ben Berning)
|
Dec
4: Quiz 4
|
16
|
Dec 7: Law of large numbers
(12.1) (presentation: Jake Roth)
|
Dec 9: Central limit theorem
(CLT) (12.2) (presentation: Nik Datsenka)
|
Dec 11: HW 12 due, review by
Prof. Alice Vatamanelu (Sergey is away)
|
17
|
|
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Final Exam (comprehensive) Friday, December 18, 3:20-5:20pm, BRWN 1154
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