The goal of the Columbia Year of Statistical Machine Learning Bootcamp Lectures is to introduce students to the computational, mathematical, and statistical foundations of data science.
The focus will be on theoretical subjects of interest in modern statistical machine learning, suitable for new Ph.D. students in computer science, statistics, applied math, and related fields.
The lectures are open (free) to all, but we kindly request that you complete the following registration form so we get an accurate headcount.
Registration: https://forms.gle/dHB5Hbq4GB43eJuJ8
Schedule:
Lectures are in the CS Auditorium (451 Computer Science Building).
The 10:15am-11:00am coffee breaks will be in the CS Lounge (also in the Computer Science Building).
Tuesday, January 14
9:15-10:15: Concentration of measure
10:15-11:00: Coffee break (CS Lounge)
11:00-12:00: Concentration of measure
12:00-2:00: Lunch break (on your own)
2:00-3:00: Concentration of measure
3:00-3:30: Break
3:30-4:30: Algorithmic applications of high-dimensional geometry
Wednesday, January 15
9:15-10:15: Algorithmic applications of high-dimensional geometry
10:15-11:00: Coffee break (CS Lounge)
11:00-12:00: Algorithmic applications of high-dimensional geometry
12:00-2:00: Lunch break (on your own)
2:00-3:00: Optimal transport
3:00-3:30: Break
3:30-4:30: Optimal transport
Thursday, January 16
9:15-10:15: Stochastic gradient methods
10:15-11:00: Coffee break (CS Lounge)
11:00-12:00: Stochastic gradient methods
12:00-2:00: Lunch break (on your own)
2:00-3:00: Stochastic gradient methods
3:00-3:30: Break
3:30-4:30: Optimal transport
Lecturers and Topics:
Jarek Błasiok : Concentration of measure
(1) Equivalence between moment bounds/MGF bounds/tail bounds, Khintchine inequality, Bernstein inequality, Johnson-Lindenstrauss for Gaussian matrices. (2) Subspace embedding: net argument, the volumetric argument for net constructions. (3) Concentration inequalities for low-influence functions.
Alex Andoni : Algorithmic applications of high-dimensional geometry
Many modern algorithms, especially for massive datasets, benefit from geometric techniques and tools even though the initial problem might have nothing to do with geometry. In this lecture series, we will cover a number of examples where (high-dimensional) geometry techniques lead to algorithms with significantly improved parameters, such as run-time, space, communication, etc. For example, starting with the classic dimension reduction method, researchers developed powerful tools for storing, transmitting, and accessing data quantums more efficiently than merely storing/etc the full data. These tools can be seen as a form of functional compression, where we store just enough information about data pieces to be useful for particular tasks. We will see applications of these tools to problems such as similarity search/nearest neighbor search, and numerical linear algebra.
Espen Bernton and Bodhi Sen : Optimal transport
Details to come.
Arian Maleki : Stochastic gradient methods
Details to come.
