Link back to the Syllabus

All readings are from the (in progress) machine learning notes. These are designed to be short, so that you can read every chapter. I recommend avoiding printing these notes, since later parts of the notes are likely to be modified (even if only a little bit).

This schedule is tentative, and is likely to change throughout the semester. Most of the lectures will be whiteboard lectures.

Week	Date	Topic	Readings
1	January 7, 2020	Introduction to the Course, started Probability	Assignment #1 released with associated code simulate.py Thought questions #1: - read Chapters 1, 2 and 3 from the notes.pdf
1	January 9, 2020	Probability (cont.)
2	January 14, 2020	Probability (cont.)
2	January 16, 2020	A First Step in Estimation: Sample Averages, Bias and Sample Complexity
3	January 21, 2020	Concentration Inequalities and Confidence
3	January 23, 2020	Formalizing Parameter Estimation, some Intro to Opt.	Thought questions #1 due Thought questions #2: - read Chapters 4, 5 and 6 from the notes.pdf
4	January 28, 2020	Formalizing Parameter Estimation (cont.)
4	January 30, 2020	MAP and MLE, and Bayesian estimation	Assignment #1 due Assignment #2 released on February 3, 2020 with associated code a2barebones.zip and tex file
5	February 4, 2020	Bayesian estimation (cont.) and conjugate priors
5	February 6, 2020	More example of posteriors, and MLE for univariate regression
6	February 11, 2020	Review
6	February 13, 2020	In-class Quiz	Thought questions #2 due Thought questions #3: - read Chapters 7, 8 and 9 from the notes.pdf
7	February 18, 2020	No classes, Reading Week
7	February 20, 2020	No classes, Reading Week
8	February 25, 2020	(Multivariate) Gradient Descent
8	February 27, 2020	Introduction to Prediction and Optimal Predictors
9	March 3, 2020	Finish Optimal Predictors, start Linear Regression and Polynomial Regression
9	March 5, 2020	Finish Linear Regression and Polynomial Regression	Assignment #2 due on March 8, 2020 Assignment #3 released with associated code a3barebones.zip and tex file
10	March 10, 2020	Midterm Review
10	March 12, 2020	Midterm, in regular lecture room (CCIS 1 140)
11	March 17, 2020	Generalization Error and Overfitting, start Evaluation of Learned Models and Hypothesis Testing
11	March 19, 2020	Regularization	Thought questions #3 due Thought questions #4: - read Chapters 10, and 11 from the notes.pdf
12	March 24, 2020	Finish regularization, and bias-variance
12	March 26, 2020	Recap conclusions about learned models and generalization error Evaluation of Learned Models and Hypothesis Testing	Demo comparing Linear Regression and Logistic Regression
13	March 31, 2020	Logistic regression and classification
13	April 2, 2020	Logistic regression and classification	Thought questions #4 due
14	April 7, 2020	Review class and Q&A (Last Class)
14	April 9, 2020	Earlier Office Hours (2 pm - 4 pm)	Assignment #3 due on Friday
Final	Wednesday, April 22, 2020, 9:00 a.m.	Final Exam	2 hours. The exam is open-book.