Daily Schedule

The following is a tentative schedule for the course. Please check back regularly for updates as the term progresses.

Day Lectures Sections in Text Brief Description
1 14 Sep (M) 1.1 Set and operations on sets
2 16 Sep (W) 1.2 Probability and properties
3 18 Sep (F) 1.6- Counting techniques
4 21 Sep (M) -1.6 Partitions
5 23 Sep (W) 1.3 Conditional probabilities
6 25 Sep (F) 1.4 Total Probability Theorems and Bayes' Rules
7 28 Sep (M) 1.5 Independence
8 30 Sep (W) 2.1-2.2- Discrete Random Variables
9 2 Oct (F) -2.2-2.3 Functions of Random Variables
04 Oct - 06 Oct Midterm 1
10 5 Oct (M) 2.4 Mean and Expectation
11 7 Oct (W) 2.4 Variance
12 9 Oct (F) 2.5 Joint PMFs of Multiple Random Variables
13 12 Oct (M) 2.6 Conditional PMFs
14 14 Oct (W) 2.7- Independence Part 1
15 16 Oct (F) -2.7 Independence Part 2
18 Oct - 20 Oct Midterm 2
16 19 Oct (M) 3.1- Continuous R.V.
17 21 Oct (W) -3.1 Mean and Variance for continuous R.V
18 23 Oct (F) 3.2 Cumulative Distribution Functions
19 26 Oct (M) 3.3 General Normal Rnadom Variables
20 28 Oct (W) 3.4 Joint PDF
21 30 Oct (F) Midterm Review
30 Oct - 2 Nov Midterm 3
22 2 Nov (M) 3.5- Conditioning
23 4 Nov (W) -3.5 More examples on conditioning
24 6 Nov (F) 3.6 The continuous Baye's Rule
25 9 Nov (M) 5.1 Markov's inequality and Chebyshev's inequality
26 11 Nov (W) 5.2 The weak law of large numbers
27 13 Nov (F) Final Review 1
28 16 Nov (M) Final Review 2
30 Nov - 3 Dec Final Exam