DSGA 1002: Statistical and Mathematical Methods
Instructor: Carlos FernandezGranda (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
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 meansquared 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 5:107 pm, CIWW 109
Recitation
Thursday 6:107 pm, CIWW 109
Office hours
Carlos: Wednesday 4:306 pm, CDS 782
Levent: Thursday 3:305 pm, WWH 605
Piazza
We will be using Piazza to answer questions and post announcements about the course. Please sign up here.
Grading policy
Homework (40%) + Midterm (20%) + Final (40%)
Books
We will provide selfcontained notes and no other texts are required. However, here are some additional references that could be useful:
Probability:
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:
Optimization:
