DS-GA 1002: Statistical and Mathematical Methods
Instructor: Carlos Fernandez-Granda (email@example.com)
TA: Levent Sagun (firstname.lastname@example.org)
This course introduces statistical and mathematical methods needed in the practice of data science. It covers basic principles in probability, statistics, linear algebra, and optimization.
Probability: Probability basics (axioms of probability, conditional probability, random variables, expectation, independence, etc.), multivariate distributions, introduction to concentration bounds, laws of large numbers, central limit theorem.
Statistics: Maximum a posteriori and maximum likelihood estimation, minimum mean-squared error estimation, confidence intervals.
Linear algebra: Vector spaces, linear transformations, singular value decomposition, eigendecomposition, principal component analysis, least squares, regression.
Optimization: Matrix calculus, gradient descent, coordinate descent, introduction to convex optimization.
Calculus and linear algebra at the undergraduate level
Monday 5:10-7 pm, CIWW 109
Thursday 6:10-7 pm, CIWW 109
Carlos: Wednesday 4:30-6 pm, CDS 782
Levent: Thursday 3:30-5 pm, WWH 605
We will be using Piazza to answer questions and post announcements about the course. Please sign up here.
Homework (40%) + Midterm (20%) + Final (40%)
We will provide self-contained notes and no other texts are required. However, here are some additional references that could be useful:
Statistical inference by Casella and Berger
All of statistics by Wasserman
Probability and Statistics by DeGroot and Schervish
Statistics by Freedman, Pisani and Purves