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 1/13	Introduction
Th 1/15	Basic probability	M4D Chapter 1.1 - 1.2
T 1/20	Conditional Probability		HW 1, Due T 2/3
Th 1/22	Bayes’ Rule	M4D 1.6 - 1.8
T 1/27	Classification and Naive Bayes
Th 1/29	Linear Algebra Basics: Vectors
T 2/3	K-means Clustering, Nearest Neighbor		HW 1 Due
Th 2/5	Maximum Likelihood Estimation
T 2/10	Bayesian Estimation		HW 2, Due T 2/24
Th 2/12	Hypothesis Testing
T 2/17	Linear Regression
Th 2/19	Linear Algebra Basics: Matrices
T 2/24	Multiple Linear Regression		HW 2 Due
Th 2/26	Basics of MCMC Sampling		Midterm Review (with distributed practice)
T 3/3	Spring Break – no class
Th 3/5	Spring Break – no class
T 3/10	Midterm Q & A
Th 3/12	Midterm Exam - In Class
T 3/17	Singular Value Decomposition (SVD)		HW 3, Due T 3/31
Th 3/19	Principal Component Analysis
T 3/24	Canonical Correlation Analysis
Th 3/26	Logistic Regression		HW 3 Due
T 3/31	Support Vector Machine		HW 4, Due T 4/21
Th 4/2	Intro to Neural Networks: Perceptron		HW 4, Due T 4/14
T 4/7	Backpropagation		HW 4, Due T 4/21
Th 4/9	Convolution & Deep Convolutional Neural Networks
T 4/14	Basics Of Transformer		HW 4 Due
Th 4/16	AutoEncoders / Variational AutoEncoders		HW 5 (optional, bonus)
T 4/21	Generative diffusion model
Th 4/23	Final Exam review		HW 5 Due
Th 4/28	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.