Syllabus
This syllabus is still under development and is subject to change.
Week  Lecture  Date  Topic  Lab  Discussion  Homework 

1  1  1/21/20 
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


2  1/23/20 
Review of Frequentist and Bayesian DecisionMakingIn 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 decisiontheoretic framework. Using this framework we can mathematically define both Bayesian and frequentist decision rules.


2  3  1/28/20 
NeymanPearson Lemma


4  1/30/20 
False Discovery Rate Control [slides]In this lecture we continue developing our decisiontheoretic 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 familywise error rate. We then look into BenjaminiHochberg which is a more elaborate method that controls the false discovery rate.


3  5  2/4/20 
Fairness in decisionmaking [slides]


6  2/6/20 
Modeling and Regression [slides]


4  7  2/11/20 
Identification Conditions for Regression [slides]



8  2/13/20 
Introduction to Bayesian Modeling


5  9  2/18/20 
Bayesian Hierarchical Models [slides]


10  2/20/20 
Approximate Inference via Sampling I [slides]


6  11  2/25/20 
Approximate Inference via Sampling II [slides]



12  2/27/20 
Application of Bayesian Inference in Biology [slides]


7  13  3/3/20 
Causal Inference I [slides]


14  3/5/20 
Causal Inference II [slides]



8  15  3/10/20 
Rudiments of Experimental Design [slides]


16  3/12/20 
Midterm



9  17  3/17/20 
Bandits: Greedy and UCB Algorithms [slides]


18  3/19/20 
No Lecture


10  19  3/23/20 
Spring Break


20  3/25/20 
Spring Break


11  21  3/31/20 
Bandits: Thompson Sampling


22  4/2/20 
Time Series Modeling [slides]


12  23  4/7/20 
Introduction to Reinforcement Learning


24  4/9/20 
QLearning and Function Approximation


13  25  4/14/20 
Neural Nets


26  4/16/20 
Bootstrap


14  27  4/21/20 
Robustness and Distribution Shift [slides]



28  4/23/20 
Privacy I [slides]


15  29  4/28/20 
Privacy II


30  4/30/20 
RealWorld Consequences of Decisions



16  31  5/5/20 
RRR week


32  5/7/20 
RRR week


17  33  5/13/20 
Final Exam
