Announcements! ( See All )
10/25 - Homework 8 deadline extended to Tue 10/27 at 11:59PM, Homework 9 is posted and due Mon 11/02 at 11:59PM!

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

Fundamentals of Inference

Week 11

Continuous Distributions

Week 12

Bias and Variance

Week 13

Introduction to Regression

Week 14

The Error in a Regression Estimate

Week 15

Inference in Regression

WeekDateContentNotes & Assignments
1 Mon 08/24 No school
Tues 08/25 No school
Wed 08/26 Lecture 1: Course introduction; fundamentals (section 1.1, 1.2) Lecture 1 Pre-lecture Notes
Lecture 1 Notes
Lecture 1 Video
HW 1 (Due Fri 09/04 at 11:59 PM)
Thu 08/27 Discussion
Fri 08/28 Lecture 2: Exact calculations, and bounds (section 1.3) Lecture 2 Pre-lecture Notes
Lecture 2 Notes
Lecture 2 Video
2 Mon 08/31 Lecture 3: Intersections of several events (section 2.1, 2.2) Lecture 3 Pre-lecture Notes
Lecture 3 Notes
Lecture 3 Video
HW 2 (Due Wed 09/09 at 11:59 PM)
Tues 09/01 Discussion
Wed 09/02 Lecture 4: Updating chances: Bayes' rule (section 2.3, 2.4) Lecture 4 Pre-lecture Notes
Lecture 4 Notes
Lecture 4 Video
Thu 09/03 Discussion
Fri 09/04 Lecture 5: A closer look at independence (section 2.4, 2.5) Lecture 5 Pre-lecture Notes
Lecture 5 Notes
Lecture 5 Video
3 Mon 09/07 Holiday HW 3 (Due Tue 09/15 at 11:59 PM)
Tues 09/08 Discussion
Wed 09/09 Lecture 6: Random variables and their distributions: the binomial (section 3.1, 3.2, 3.3) Lecture 6 Pre-lecture Notes
Lecture 6 Notes
Lecture 6 Video
Thu 09/10 Discussion
Fri 09/11 Lecture 7: Simple random sampling (section 3.4, 3.5) Lecture 7 Pre-lecture Notes
Lecture 7 Notes
Lecture 7 Video
Lecture 7 (Part II) Pre-lecture Notes
Lecture 7 (Part II) Notes
Lecture 7 (Part II) Video
4 Mon 09/14 Lecture 8: Exponential approximations (section 3.5, 4.1) Lecture 8 Pre-lecture Notes
Lecture 8 Notes
Lecture 8 Video
HW 4 (Due Mon 09/21 at 11:59 PM)
Tues 09/15 Discussion
Wed 09/16 Lecture 9: Waiting Time (section 4.2, 4.3) Lecture 9 Pre-lecture Notes
Lecture 9 Notes
Lecture 9 Video
Thu 09/17 Discussion
Fri 09/18 Lecture 10: Exponential approximation and Poisson Distribution (section 4.3, 4.4) Lecture 10 Pre-lecture Notes
Lecture 10 Notes
Lecture 10 Video
5 Mon 09/21 Lecture 11: Poisson and Expectation (section 4.4, 5.1) Lecture 11 Pre-lecture Notes
Lecture 11 Notes
Lecture 11 Video
HW 5 (Due Mon 09/28 at 11:59 PM)
Tues 09/22 Discussion
Wed 09/23 Lecture 12: Expectation and Joint Distribution; (section 5.1, 5.2, 5.3) Lecture 12 Pre-lecture Notes
Lecture 12 Notes
Lecture 12 Video
Thu 09/24 Discussion
Fri 09/25 Lecture 13: The method of indicators and Additivity of Expectation (section 5.3) Lecture 13 Pre-lecture Notes
Lecture 13 Notes
Lecture 13 Video
6 Mon 09/28 Lecture 14: Indicators and Updating revisited: conditional distribution (section 5.3, 5.4) Lecture 14 Pre-lecture Notes
Lecture 14 Notes
Lecture 14 Video
HW 6 (Due Wed 10/07 at 11:59 PM)
Tues 09/29 Discussion
Wed 09/30 Lecture 15: Unbiased biased estimator(section 5.4, 5.5) Lecture 15 Pre-lecture Notes
Lecture 15 Notes
Lecture 15 Video
Thu 10/01 Discussion
Fri 10/02 Lecture 16: Expectation by conditioning (section 5.5) Lecture 16 Pre-lecture Notes
Lecture 16 Notes
Lecture 16 Video
7 Mon 10/05 Lecture 17: Examples: putting things together Lecture 17 Pre-lecture Notes
Lecture 17 Notes
Lecture 17 Video
Tues 10/06 Discussion
Wed 10/07 Midterm review Lecture 18 Pre-lecture Notes
Lecture 18 Notes
Lecture 18 Video
Thu 10/08 Discussion
Fri 10/09 Midterm
8 Mon 10/12 Lecture 20: Measuring variability (section 6.1, 6.2) Lecture 20 Pre-lecture Notes
Lecture 20 Notes
Lecture 20 Video
HW 7 (Due Mon 10/19 at 11:59 PM)
Tues 10/13 Discussion
Wed 10/14 Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4) Lecture 21 Pre-lecture Notes
Lecture 21 Notes
Lecture 21 Video
Thu 10/15 Discussion
Fri 10/16 Lecture 22: Variability of a random sample sum (section 7.1) Lecture 22 Pre-lecture Notes
Lecture 22 Notes
Lecture 22 Video
9 Mon 10/19 Lecture 23: The accuracy of a simple random sample (section 7.2) Lecture 23 Pre-lecture Notes
Lecture 23 Notes
Lecture 23 Video
HW 8 (Due Mon 10/26 at 11:59 PM)
Tues 10/20 Discussion
Wed 10/21 Lecture 24: The law of averages (section 7.3) Lecture 24 Pre-lecture Notes
Lecture 24 Notes
Lecture 24 Video
Thu 10/22 Discussion
Fri 10/23 Lecture 25: Central Limit Theorem (section 8.1, 8.2) Lecture 25 Pre-lecture Notes
Lecture 25 Notes
Lecture 25 Video
10 Mon 10/26 Lecture 26: Normal approximations (section 8.3, 8.4) Lecture 26 Pre-lecture Notes
Lecture 26 Notes
Lecture 26 Video
HW 9 (Due Mon 11/02 at 11:59 PM)
Tues 10/27 Discussion
Wed 10/28 Lecture 27: Inference: testing hypotheses (section 9.1)
Thu 10/29 Discussion
Fri 10/30 Lecture 28: Inference: testing hypotheses (section 9.2) Continued
11 Mon 11/02 Lecture 29: Confidence intervals continue (section 9.3, 9.4)
Tues 11/03 Discussion
Wed 11/04 Lecture 30: Probability density (section 10.1, 10.2)
Thu 11/05 Discussion
Fri 11/06 Lecture 31: The exponential distribution (section 10.3)
12 Mon 11/09 Lecture 32: Some properties of the normal distribution (section 10.4)
Tues 11/10 Discussion
Wed 11/11 Holiday
Thu 11/12 Discussion
Fri 11/13 Lecture 33: Prediction: bias-variance (section 11.1)
13 Mon 11/16 Lecture 34: The bias-variance tradeoff (section 11.2)
Tues 11/17 Discussion
Wed 11/18 Lecture 35: Correlation (section 11.3, 11.4)
Thu 11/19 Discussion
Fri 11/20 Lecture 36: The regression estimates, effect and fallacy (section 11.4, 11.5)
14 Mon 11/23 Lecture 37: The regression model for data (section 12.1)
Tues 11/24 Discussion
Wed 11/25 Holiday
Thu 11/26 Holiday
Fri 11/27 Holiday
15 Mon 11/30 Lecture 38: Inference for the true slope in the regression model (section 12.2, section 12.3)
Tues 12/01 Discussion
Wed 12/02 Lecture 39: Conclusion
Thu 12/03 Discussion
Fri 12/04 Lecture 40: Review