Schedule

Download this: Guide to Math Notation in Jupyter

“M4D” in the reading refers to Mathematical Foundations for Data Analysis, by Jeff Phillips

Day	Title / Notes	Reading	Homework
W 1/17	Introduction
M 1/22	Basic probability	M4D Chapter 1.1 - 1.2
W 1/24	Conditional Probability		HW 1, Due W 2/7
M 1/29	Bayes’ Rule	M4D 1.6 - 1.8
W 1/31	Classification and Naive Bayes
M 2/5	Linear Algebra Basics: Vectors
W 2/7	K-means Clustering, Nearest Neighbor		HW 1 Due
M 2/12	Maximum Likelihood Estimation
W 2/14	Bayesian Estimation		HW 2, Due W 2/26
M 2/19	Hypothesis Testing
W 2/21	Linear Regression
M 2/26	Midterm Review		HW 2 Due
W 2/28	Midterm Exam - In Class
M 3/4	Spring Break – no class
W 3/6	Spring Break – no class
M 3/11	Linear Algebra Basics: Matrices
W 3/13	Multiple Linear Regression		HW 3, Due W 3/27
M 3/18	Multiple Linear Regression cont.
W 3/20	Singular Value Decomposition (SVD)
M 3/25	Principal Component Analysis
W 3/27	Canonical Correlation Analysis		HW 3 Due
M 4/1	Logistic Regression
W 4/3	Logistic Regression, cont.		HW 4, Due W 4/17
M 4/8	Intro to Neural Networks: Perceptron
W 4/10	Backpropagation
M 4/15	Convolution
W 4/17	Deep Convolutional Neural Networks		HW 4 Due
M 4/22	AutoEncoders / Variational AutoEncoders
W 4/24	Generative models: diffusion model
M 4/29	Final Exam - Take Home

Disclaimer

The instructor reserves the right to make changes to the course schedule, syllabus, and project deadlines. Changes will be announced early in advance.