# DS-GA 1002: Statistical and Mathematical Methods

Instructor: Carlos Fernandez-Granda (cfgranda@cims.nyu.edu)
TA: Levent Sagun (sagun@cims.nyu.edu)

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.

## Announcements

• The final will take place on Monday December 14 from 5pm to 8pm in SILVER 207 (128), not in the usual classroom

## Syllabus

• 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.

## Prerequisites

Calculus and linear algebra at the undergraduate level

## General Information

### Lecture

Monday 5:10-7 pm, CIWW 109

### Recitation

Thursday 6:10-7 pm, CIWW 109

### Office hours

Carlos: Wednesday 4:30-6 pm, CDS 782
Levent: Thursday 3:30-5 pm, WWH 605

### Piazza

Homework (40%) + Midterm (20%) + Final (40%)

### Books

We will provide self-contained notes and no other texts are required. However, here are some additional references that could be useful:

• Probability:

• A first course in probability by Ross

• Introduction to Probability by Bertsekas and Tsitsiklis

• Statistics:

• Statistical inference by Casella and Berger

• All of statistics by Wasserman

• Probability and Statistics by DeGroot and Schervish

• Statistics by Freedman, Pisani and Purves

• Linear algebra:

• Linear Algebra and Its Applications by Strang

• Optimization: