## Info

#### Instructor

**When**: Tue, Thu 5pm-6.15pm**Where**: CAS-211**Prof**: Babis Tsourakakis**Email**: ctsourak@bu.edu**Office hours**(CDS 912): Tu 12-1pm, Th 10-11am

#### Teaching Fellow

**TF**: Mr. Tiany Chen**Email**: ctony@bu.edu**Labs**: schedule**Office hours**(CDS 362): Wed 2:00-3:30 pm, Fri 10:00-11:30 am

## Piazza website

## Github

Prerequisites

Students taking this class must have taken:

- CS 112
- CS 131 (MA293)
- CS 132 (MA242)
- and CS 237 (MA581) or equivalent.

This year the prerequisites will be strictly enforced. CS 330 is *highly *recommended but not a prereq.

## Syllabus

Topics will include probability, information theory, linear algebra, calculus, Fourier analysis, graph theory with a strong focus on their applicability for analyzing datasets. Finally, two lectures will be devoted to data management, and more specifically the classic relational model, SQL and Datalog. A detailed syllabus is available on Piazza.

## Textbooks

There will be assigned readings from the following books that are available online (click for the pdf)

- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
- Foundations of Data Science by Avrim Blum, John Hopcroft, Ravi Kannan
- Understanding Machine Learning: From theory to algorithms by Shai Shalev-Shwartz and Shai Ben-David
- Introduction to Probability for Data Science by Stanley Chan

## Programming

The class assumes familiarity with programming. The recommended languages for this class are Python3 and Julia. R and Matlab are also recommended. Other languages are welcome (C, C++, Java, etc), but are not recommended for this class.

## Lectures

**Note**: at the end of each lecture, you will find the assigned readings. The readings associated with a magnifying glass are mandatory. The rest is material if you are further interested, and have the time to devote.

**Lecture 1 (1/19)**: data visualization – introduction, class logistics, types of data, basics of data visualization

Slides available here.**Lecture 2 (1/25)**: probability I – review of prerequisite material, and other basic concepts through problem solving

Slides available here.**Lecture 3 (1/26)**:probability II – convergence of random variables, Markov’s inequality

Slides available here.**Lecture 4 (2/1)**: probability III – Weak law of large numbers, confidence intervals, π estimation randomized algorithm Central Limit theorem

Slides available here.

## Assignments

- Homework 1 (to be released on 1/27, due to 2/3)