Announcements! ( See All )
4/14 - HW 11 is due Monday 04/19 at 11:59 PM.

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

Spring Recess

Week 11

Fundamentals of Inference

Week 12

Continuous Distributions

Week 13

Bias and Variance

Week 14

The Error in a Regression Estimate

Week 15

Inference in Regression

WeekDateContentNotes & Assignments
1 Mon 01/18 No school
Tues 01/19 No school
Wed 01/20 Lecture 1: Course introduction and the basics (Sections 1.1, 1.2) Lecture 1 Pre-lecture Notes
Lecture 1 Notes
Lecture 1 Video
HW 1 (Due Fri 01/29 at 11:59 PM)
Thu 01/21 Discussion
Fri 01/22 Lecture 2: Axioms of Probability (Section 1.3) Lecture 2 Pre-lecture Notes
Lecture 2 Notes
Lecture 2 Video
2 Mon 01/25 Lecture 3: Axioms of Probability, Intersections (section 1.3, 2.1) Lecture 3 Pre-lecture Notes
Lecture 3 Notes
Lecture 3 Video
HW 2 (Due Mon 02/01 at 11:59 PM)
Tues 01/26 Discussion
Wed 01/27 Lecture 4: Symmetry in Sampling, Bayes’ Rule (section 2.2, 2.3) Lecture 4 Pre-lecture Notes
Lecture 4 Notes
Lecture 4 Kahoot
Lecture 4 Video
Thu 01/28 Discussion
Fri 01/29 Lecture 5: Bayes’ Rule: definition, use, and interpretation, Independence (section 2.2, 2.3, 2.5) Lecture 5 Pre-lecture Notes
Lecture 5 Notes
Lecture 5 Video
Exam-prep Worksheet 1
3 Mon 02/01 Lecture 6: Finish up chapter 2, Random variables & their distributions (section 2.4, 3.1, 3.2) Lecture 6 Pre-lecture Notes
Lecture 6 Notes
Lecture 6 Video
HW 3 (Due Mon 02/08 at 11:59 PM)
Tues 02/02 Discussion
Wed 02/03 Lecture 7: Random variables & their distributions + 2 special distributions (section 3.1, 3.2, 3.3, 3.4) Lecture 7 Pre-lecture Notes
Lecture 7 Notes
Lecture 7 Jupyter Notebook
Lecture 7 Video
Thu 02/04 Discussion
Fri 02/05 Lecture 8: The hypergeometric distribution, examples, CDF (section 3.4, 3.5, 4.1) Lecture 8 Pre-lecture Notes
Lecture 8 Notes
Lecture 8 Video
4 Mon 02/08 Lecture 9: CDF, Waiting Times (section 3.5, 4.1, 4.2) Lecture 9 Pre-lecture Notes
Lecture 9 Notes
Lecture 9 Video
HW 4 (Due Wed 02/17 at 11:59 PM)
Tues 02/09 Discussion
Wed 02/10 Lecture 10: Waiting Times, Exponential Approximations (section 4.2, 4.3) Lecture 10 Pre-lecture Notes
Lecture 10 Notes
Lecture 10 Video
Thu 02/11 Discussion
Fri 02/12 Lecture 11: Waiting times, exponential approximations, the Poisson distribution (section 4.3, 4.4) Lecture 11 Pre-lecture Notes
Lecture 11 Notes
Lecture 11 Video
5 Mon 02/15 No school
Tues 02/16 Discussion
Wed 02/17 Lecture 12: Sums of Poisson RVs, Expectation, Functions of RVs (section 4.4, 5.1, 5.2) Lecture 12 Pre-lecture Notes
Lecture 12 Notes
Lecture 12 Video
HW 5 (Due Tue 02/23 at 11:59 PM)
Thu 02/18 Discussion
Fri 02/19 Lecture 13: Functions of RVs, Method of indicators (section 5.2, 5.3) Lecture 13 Pre-lecture Notes
Lecture 13 Notes
Lecture 13 Video
6 Mon 02/22 Lecture 14: Method of indicators, definition of unbiased estimators (section 5.3, 5.4) Lecture 14 Pre-lecture Notes
Lecture 14 Notes
Lecture 14 Video
HW 6 (Due Mon 03/01 at 11:59 PM)
Tues 02/23 Discussion
Wed 02/24 Lecture 15: Wrap up method of indicators, unbiased estimators (section 5.3, 5.4) Lecture 15 Pre-lecture Notes
Lecture 15 Notes
Lecture 15 Video
Thu 02/25 Discussion
Fri 02/26 Lecture 16: Conditional expectation and expectation by conditioning (section 5.5, 5.6) Lecture 16 Pre-lecture Notes
Lecture 16 Notes
Lecture 16 Video
7 Mon 03/01 Lecture 17: Expectation by conditioning (section 5.6) Lecture 17 Pre-lecture Notes
Lecture 17 Notes
Lecture 17 Video
Tues 03/02 Discussion
Wed 03/03 Lecture 18: Midterm review Lecture 18 Pre-lecture Notes
Lecture 18 Notes
Lecture 18 Video
Thu 03/04 Discussion
Fri 03/05 Midterm
8 Mon 03/08 Lecture 20: Measuring variability (section 6.1, 6.2) Lecture 20 Pre-lecture Notes
Lecture 20 Notes
Lecture 20 Video
HW 7 (Due Mon 03/15 at 11:59 PM)
Tues 03/09 Discussion
Wed 03/10 Lecture 21: Markov and Chebyshev’s Inequalities (section 6.3, 6.4) Lecture 21 Pre-lecture Notes
Lecture 21 Notes
Lecture 21 Video
Thu 03/11 Discussion
Fri 03/12 Lecture 22: Markov and Chebyshev’s Inequalities problems, Sums of RVs (section 6.3, 6.4, 7.1) Lecture 22 Pre-lecture Notes
Lecture 22 Notes
Lecture 22 Video
9 Mon 03/15 Lecture 23: Sums of RVs and sampling without replacement (section 7.1, 7.2) Lecture 23 Pre-lecture Notes
Lecture 23 Notes
Lecture 23 Video
HW 8 (Due Mon 03/29 at 11:59 PM)
Tues 03/16 Discussion
Wed 03/17 Lecture 24: Sampling without replacement and the Law of Averages (section 7.2, 7.3) Lecture 24 Pre-lecture Notes
Lecture 24 Notes
Lecture 24 Video
Thu 03/18 Discussion
Fri 03/19 Lecture 25: The Law of Averages & setting up Chapter 8 (section 7.3) Lecture 25 Pre-lecture Notes
Lecture 25 Notes
Lecture 25 Video
10 Mon 03/22 No school
Tues 03/23 No school
Wed 03/24 No school
Thu 03/25 No school
Fri 03/26 No school
11 Mon 03/29 Lecture 26: The Central Limit Theorem (section 8.1, 8.2) Lecture 26 Pre-lecture Notes
Lecture 26 Notes
Lecture 26 Video
HW 9 (Due Mon 04/05 at 11:59 PM)
Tues 03/30 Discussion
Wed 03/31 Lecture 27: The Normal distribution & using the Central Limit Theorem (section 8.2, 8,3) Lecture 27 Pre-lecture Notes
Lecture 27 Notes
Lecture 27 Video
Thu 04/01 Discussion
Fri 04/02 Lecture 28: Finish Chapter 8 and Hypothesis Tests (section 8.4, 9.1, 9.2) Lecture 28 Pre-lecture Notes I
Lecture 28 Pre-lecture Notes II
Lecture 28 Notes I
Lecture 28 Notes II
Lecture 28 Video
12 Mon 04/05 Lecture 29: A/B Testing (section 9.2) Lecture 29 Pre-lecture Notes
Lecture 29 Notes
Lecture 29 Video
HW 10 (Due Mon 04/12 at 11:59 PM)
Tues 04/06 Discussion
Wed 04/07 Lecture 30: Confidence Intervals (section 9.3, 9.4) Lecture 30 Pre-lecture Notes
Lecture 30 Notes
Lecture 30 Video
Thu 04/08 Discussion
Fri 04/09 Lecture 31: Finish Confidence Intervals; Densities (section 9.4, 10.1) Lecture 31 Pre-lecture Notes
Lecture 31 Notes
Lecture 31 Video
13 Mon 04/12 Lecture 32: Continuous Distributions: Densities, Expectation, and Variance (section 10.1, 10.2, 10.3) Lecture 32 Notes
Lecture 32 Video
HW 11 (Due Mon 04/19 at 11:59 PM)
Tues 04/13 Discussion
Wed 04/14 Lecture 33: Normal Distributions, Prediction: bias-variance (section 10.4, 11.1) Lecture 33 Notes
Lecture 33 Video
Thu 04/15 Discussion
Fri 04/16 Lecture 34: (Asynchronous)The bias-variance tradeoff, Continued (section 11.1, 11.2)
14 Mon 04/19 Lecture 35: Correlation (section 11.3, 11.4)
Tues 04/20 Discussion
Wed 04/21 Lecture 36: The regression estimates, effect and fallacy (section 11.4, 11.5)
Thu 04/22 Discussion
Fri 04/23 Lecture 37: The regression model for data (section 12.1)
15 Mon 04/26 Lecture 38: Inference for the true slope in the regression model (section 12.2, section 12.3)
Tues 04/27 Discussion
Wed 04/28 Lecture 39: Conclusion
Thu 04/29 Discussion
Fri 04/30 Lecture 40: Review