|
DATE
|
Topics
and Activities
|
Assignments
|
| Jan
13 |
. |
.
|
Jan
15 |
. |
.
|
| Jan
22 |
. |
.
|
| Jan
27 |
Homework
Assignment #1 |
Assignment
#1: 1.2.1, 1.3.2, 1.4.5, 1.5.2, 1.7.4, 1.9.1
|
| Jan
29 |
Contest
#1 |
.
|
| Feb
3 |
Homework
Assignment #2 |
Assignment
#2: 2.2.4, 2.3.3, 2.4.1, 2.5.2, 2.6.6, 2.8.1
|
| Feb
5 |
Contest
#2 |
.
|
| Feb
10 |
Homework
Assignment #3 |
Assignment
#3: 3.1.1, 3.2.2, 3.4.1, 3.6.1, 3.7.1, 3.8.3
|
| Feb
12 |
Contest
#3 |
.
|
| Feb
17 |
Homework
Assignment #4 |
Assignment
#4: 4.2.4, 4.3.2, 4.4.1, 4.5.1, 4.7.3, 4.8.4
|
| Feb
19 |
Contest
#4 |
.
|
| Feb
24 |
. |
.
|
| Feb
26 |
MID-TERM
Examination
Closed book; Closed notes; 1 sheet of Formulae |
.
|
| Mar
5 |
Homework
Assignment #5 |
Assignment
#5 5.1.1, 5.2.1, 5.4.2, 5.6.2, 5.7.1, 5.9.2
|
| Mar
10-12 |
. |
.
|
| Mar
17 |
Contest
#5 |
.
|
| Mar
19 |
. |
.
|
| Mar
26 |
Homework
Assignment #6 |
Assignment
#6: 6.2.4, 6.3.1, 6.4.1, 6.5.1, 6.7.1, 6.8.1
|
| Mar
31 |
Contest
#6 |
.
|
| Apr
2 |
Homework
Assignment #7 |
Assignment
#7: 7.1.3, 7.2.1, 7.3.1, 7.4.2, 7.6.2, 7.7.2
|
| Apr
7 |
Contest
#7 |
.
|
| Apr
7 |
. |
.
|
| Apr
9 |
Contest
#8 |
.
|
| Apr
14 |
Homework
Assignment #8 |
Assignment
#8: 8.1.1, 8.2.1, 8.2.2, 8.2.3, 8.3.1, 8.3.2
|
| Apr
16 |
Contest
#9 |
.
|
| Apr
16 |
Project
Due |
Project
Due
|
| Apr
21 |
. |
.
|
| Apr
23 |
Homework
Assignment #9 |
Assignment
#9: 9.1.2, 9.2.2, 9.3.1, 9.4.1, 9.4.2, 9.5.3
|
| Apr
28 |
. |
.
|
| May
2 |
FINAL
EXAMINATION 12:30 pm-3:30 pm
Closed Book; Closed Notes; 3 sheets of Formulae |
.
|
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about email account requirements.
|
Detail
of Course Content
|
|
Topic
|
Description
|
Sections
from Text
|
|
Probability
|
Probability
Axioms, Conditional Probability, Independence, Tree Diagrams,
Probability Counting, Independent Trials, Reliability Theory
|
Chpt.
1
|
|
Discrete
R.V
|
Probability
Mass Function (PMF). Discrete Random Variables, Cumulative Distribution
Function (CDF), Functions of a R.V, Expected values, Variance,
Special discrete PMF's, Conditional PMF
|
Chpt.
2
|
|
Cont.
R. V
|
CDF,
Probability Density Function (PDF), Expected Values, Variance,
Special PDF's, Gaussian PDF, PDF's for Derived R.V's, Conditional
PDF
|
Chpt
4
|
|
Multiple
Cont.
|
Joint
CDF, Joint PDF, Marginal PDF, Functions of 2 R.V, Expected R.V
Values, Variance, Conditional PDF, Independent R.V's, Jointly
Gaussian R.V
|
Chpt
5
|
|
Stochastic
Processes
|
iid's,
expected values, correlations, stationary and wide sense stationary
processes
|
Chpt.
6
|
|
Sums
of R.V
|
PDF
of sums of 2 r.v's, Moment Generating Function (MGF), iid, MGF
for iid, Sums of Gaussian r.v's, Central Limit Theorem
|
Chpt.
7
|
|
Sample
Mean
|
expected
value, variance, Markov, Chebyshev, Chernoff, Laws of Large numbers
|
Chpt.
8
|
|
Statistical
Inference
|
Significance
testing, hypothesis testing MAP, ML
|
Chpt.
9
|
|