Syllabus
⚠️ This content is archived as of March 2026 and is retained exclusively for reference. Find current offerings.
This syllabus is still under development and is subject to change.
| Week | Lecture | Date | Topic | Lab | Discussion | Homework |
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| 1 | 1 | 8/27/20 |
Course Overview, Making Decisions Under Uncertainty [slides]In this lecture we provide an overview of data science and where it might head in the future. We discuss the importance of decision making rather than simple inference. As a first step in this direction we cover the different angles through which a simple hypothesis test can be viewed.
Readings
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| 2 | 9/1/20 |
Review of Frequentist and Bayesian Decision-MakingIn this lecture we continue investigating the intricacies that arise when running a simple hypothesis test. We look at the difference between the frequentist and the Bayesian view of hypothesis testing. We further develop these ideas by introducing a decision-theoretic framework. Using this framework we can mathematically define both Bayesian and frequentist decision rules.
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| 2 | 3 | 9/3/20 |
Neyman-Pearson Lemma
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| 4 | 9/8/20 |
False Discovery Rate Control [slides]In this lecture we continue developing our decision-theoretic framework. In particular we consider properties of the risk of various loss functions. The main portion of the lecture is dedicated to false discovery rate control. We look at two ideas that go beyond basic hypothesis testing. We consider the Bonferroni test which is a naive way of controlling the family-wise error rate. We then look into Benjamini-Hochberg which is a more elaborate method that controls the false discovery rate.
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| 3 | 5 | 9/10/20 |
Fairness in decision-making [slides]
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| 6 | 9/15/20 |
Modeling and Regression [slides]
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| 4 | 7 | 9/17/20 |
Identification Conditions for Regression [slides]
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| 8 | 9/22/20 |
Introduction to Bayesian Modeling
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| 5 | 9 | 9/24/20 |
Bayesian Hierarchical Models [slides]
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| 10 | 9/29/20 |
Approximate Inference via Sampling I [slides]
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| 6 | 11 | 10/1/20 |
Approximate Inference via Sampling II [slides]
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| 12 | 10/6/20 |
Application of Bayesian Inference in Biology [slides]
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| 7 | 13 | 10/8/20 |
Causal Inference I [slides]
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| 14 | 10/13/20 |
Causal Inference II [slides]
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| 8 | 15 | 10/15/20 |
Rudiments of Experimental Design [slides]
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| 16 | 10/20/20 |
Midterm
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| 9 | 17 | 10/22/20 |
Bandits: Greedy and UCB Algorithms [slides]
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| 18 | 10/27/20 |
No Lecture
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| 10 | 19 | 10/29/20 |
Bandits: Thompson Sampling
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| 20 | 11/3/20 |
Time Series Modeling [slides]
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| 11 | 21 | 11/5/20 |
Introduction to Reinforcement Learning
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| 22 | 11/10/20 |
Q-Learning and Function Approximation
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| 12 | 23 | 11/12/20 |
Neural Nets
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| 24 | 11/17/20 |
Bootstrap
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| 13 | 25 | 11/19/20 |
Robustness and Distribution Shift [slides]
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| 26 | 11/24/20 |
Privacy I [slides]
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| 14 | 27 | 11/26/20 |
Thanksgiving Break
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| 28 | 12/1/20 |
Privacy II
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| 15 | 29 | 12/3/20 |
Real-World Consequences of Decisions
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| 30 | 12/8/20 |
RRR week
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| 16 | 31 | 12/10/20 |
RRR week
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| 32 | 12/15/20 |
Final Exam
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