Announcements! ( See All )
3/2 - Midterm on Friday March 6th. Read midterm info on Piazza!

Week 1

Getting Started

Week 2

Conditioning

Week 3

Random Variables

Week 4

Random Variables and Expectation

Week 5

The Power of Additivity

Week 6

Path to Prediction

Week 7

Midterm Week

Week 8

Variability

Week 9

Large Random Samples

Week 10

Holiday

Week 11

Fundamentals of Inference

Week 12

Continuous Distributions

Week 13

Bias and Variance

Week 14

Introduction to Regression

Week 15

Inference in Regression

WeekDateContentNotes & Assignments
1 Mon 01/20 No school
Tues 01/21 No school
Wed 01/22 Lecture 1: Course introduction and fundamentals (section 1.1, 1.2) Lecture 1 Notes
HW 1 (Due Mon 01/27 at 10 PM)
Thu 01/23 Discussion
Fri 01/24 Lecture 2: Exact calculations and bounds (section 1.3) Lecture 2 Notes
2 Mon 01/27 Lecture 3: Intersections of several events (section 2.1, 2.2) HW 2 (Due Mon 02/03 at 10 PM)
Lecture 3 Notes
Tues 01/28 Discussion
Wed 01/29 Lecture 4: Updating chances: Bayes' rule (section 2.3, 2.4) Lecture 4 Notes
Thu 01/30 Discussion
Fri 01/31 Lecture 5: A closer look at independence (section 2.4, 2.5) Lecture 5 Notes
3 Mon 02/03 Lecture 6: Random variables and their distributions: the binomial (section 3.1, 3.2, 3.3) HW 3 (Due Mon 02/10 at 10 PM)
Lecture 6 Notes
Tues 02/04 Discussion
Wed 02/05 Lecture 7: Simple random sampling (section 3.4, 3.5) Lecture 7 Notes
Thu 02/06 Quiz 1
Fri 02/07 Lecture 8: Exponential approximations (section 3.5, 4.1) Lecture 8 Notes
4 Mon 02/10 Lecture 9: Waiting time (section 4.2, 4.3) HW 4 (Due Tue 02/18 at 10 PM)
Lecture 9 Notes
Tues 02/11 Discussion
Wed 02/12 Lecture 10: Exponential approximation and Poisson Distribution (section 4.3, 4.4) Lecture 10 Notes
Thu 02/13 Discussion
Fri 02/14 Lecture 11: Poisson and Expectation (section 4.4, 5.1) Lecture 11 Notes
Lecture 11 Video Part1
Lecture 11 Video Part2
5 Mon 02/17 Holiday HW 5 (Due Mon 02/24 at 10 PM)
Tues 02/18 Discussion
Wed 02/19 Lecture 12: Expectation and Joint Distribution; (section 5.1, 5.2, 5.3) Lecture 12 Notes
Thu 02/20 Discussion
Fri 02/21 Lecture 13: The method of indicators and Additivity of Expectation (section 5.3) Lecture 13 Notes
6 Mon 02/24 Lecture 14: Indicators and Updating revisited: conditional distribution (section 5.3, 5.4) Lecture 14 Notes
HW 6 (Due Mon 03/02 at 10 PM)
Tues 02/25 Discussion
Wed 02/26 Lecture 15: Unbiased biased estimator(section 5.4, 5.5) Lecture 15 Notes
Thu 02/27 Quiz 2
Fri 02/28 Lecture 16: Expectation by conditioning (section 5.5) Lecture 16 Notes
7 Mon 03/02 Lecture 17: examples: putting things together Midterm Information
Lecture 17 Notes
Tues 03/03 Discussion
Wed 03/04 Midterm review Lecture 18 Notes
Thu 03/05 Discussion
Fri 03/06 Midterm
8 Mon 03/09 Lecture 20: Measuring variability (section 6.1, 6.2) Lecture 20 Notes
HW 7 (Due Mon 03/16 at 10 PM)
Tues 03/10 Discussion
Wed 03/11 Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4) Lecture 21 Notes
Lecture 21 Recording
Thu 03/12 Discussion
Fri 03/13 Lecture 22: Variability of a random sample sum (section 7.1) Lecture 22 Notes
Lecture 22 Recording
9 Mon 03/16 Lecture 23: The accuracy of a simple random sample (section 7.2) HW 8 (Due Mon 03/30 at 10 PM)
Lecture 23 Notes
Lecture 23 Recording
Tues 03/17 Discussion
Wed 03/18 Lecture 24: The law of averages (section 7.3) Lecture 24 Notes
Lecture 24 Recording
Thu 03/19 Discussion
Fri 03/20 Lecture 25: Central Limit Theorem (section 8.1, 8.2) Lecture 25 Notes
Lecture 25 Recording
10 Mon 03/23 Holiday
Tues 03/24 Holiday
Wed 03/25 Holiday
Thu 03/26 Holiday
Fri 03/27 Holiday
11 Mon 03/30 Lecture 26: Normal approximations (section 8.3, 8.4) HW 9 (Due Mon 04/06 at 10 PM)
Lecture 26 Recording
Lecture 26 Notes
Tues 03/31 Discussion
Wed 04/01 Lecture 27: Inference: testing hypotheses (section 9.1, 9.2)
Thu 04/02 Quiz 3
Fri 04/03 Lecture 28: Confidence intervals (section 9.3)
12 Mon 04/06 Lecture 27: Confidence intervals continue (section 9.4)
Tues 04/07 Discussion
Wed 04/08 Lecture 28: Probability density (section 10.1, 10.2)
Thu 04/09 Discussion
Fri 04/10 Lecture 29: The exponential distribution (section 10.3)
13 Mon 04/13 Lecture 30: Some properties of the normal distribution (section 10.4)
Tues 04/14 Discussion
Wed 04/15 Lecture 31: Prediction: bias-varaince (section 11.1)
Thu 04/16 Discussion
Fri 04/17 Lecture 32: The bias-variance tradeoff (section 11.2)
14 Mon 04/20 Lecture 33: Correlation (section 11.3, 11.4)
Tues 04/21 Discussion
Wed 04/22 Lecture 34: The regression estimates, effect and fallacy (section 11.4, 11.5)
Thu 04/23 Quiz 4
Fri 04/24 Lecture 35: The regression model for data (section 12.1)
15 Mon 04/27 Lecture 36: Inference for the true slope in the regression model (section 12.2, 12.3)
Tues 04/28 Discussion
Wed 04/29 Lecture 37: Conclusion
Thu 04/30 Discussion
Fri 05/01 Lecture 38: Review