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
T 1/14	Introduction
Th 1/16	Basic probability	M4D Chapter 1.1 - 1.2
T 1/21	Conditional Probability		HW 1, Due T 2/4
Th 1/23	Bayes’ Rule	M4D 1.6 - 1.8
T 1/28	Classification and Naive Bayes
Th 1/30	Linear Algebra Basics: Vectors
T 2/4	K-means Clustering, Nearest Neighbor		HW 1 Due
Th 2/6	Maximum Likelihood Estimation
T 2/11	Bayesian Estimation		HW 2, Due T 2/25
Th 2/13	Hypothesis Testing
T 2/18	Linear Regression
Th 2/20	Linear Algebra Basics: Matrices
T 2/25	Multiple Linear Regression		HW 2 Due
Th 2/27	Midterm Review
T 3/4	Basics of MCMC Sampling
Th 3/6	Midterm Exam - In Class
T 3/11	Spring Break – no class
Th 3/13	Spring Break – no class
T 3/18	Singular Value Decomposition (SVD)		HW 3, Due T 4/1
Th 3/20	Principal Component Analysis
T 3/25	Canonical Correlation Analysis
Th 3/27	Logistic Regression
T 4/1	Logistic Regression, cont.		HW 3 Due
Th 4/3	Intro to Neural Networks: Perceptron
T 4/8	Backpropagation		HW 4, Due T 4/22
Th 4/10	Convolution
T 4/15	Deep Convolutional Neural Networks
Th 4/17	AutoEncoders / Variational AutoEncoders
T 4/22	Generative models: diffusion model		HW 4 Due
Th 4/24	Final Exam review
Th 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.