MGT-302 / 2 credits

Teacher(s): Etesami Seyed Jalal, Malamud Semyon, Kiyavash Negar

Language: English


Summary

This course focuses on on methods and algorithms needed to apply machine learning with an emphasis on applications in business analytics.

Content

Keywords

machine learning, causal inference, time series, asset pricing

Learning Prerequisites

Required courses

A course in basic probability theory

A course in basic linear algebra

Calculus

Familiarity with Python or Matlab

Important concepts to start the course

Students should be familiar with basic concepts of probability theory, calculus, linear algebra, and programming.

Learning Outcomes

By the end of the course, the student must be able to:

  • Formulate supervised and unsupervised learning problems and apply it to data

Transversal skills

  • Assess one's own level of skill acquisition, and plan their on-going learning goals.

Teaching methods

Formal teaching interlaced with practical exercices.

Expected student activities

Attending lectures and working on homework and projects.

Assessment methods

Three homeworks (33.33333333% each)

Supervision

Office hours Yes
Assistants Yes
Forum No

In the programs

  • Semester: Spring
  • Number of places: 80
  • Exam form: During the semester (summer session)
  • Subject examined: Data driven business analytics
  • Lecture: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22