MGT-416 / 4 credits

Teacher: Kiyavash Negar

Language: English


Summary

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data throughout the course.

Content

Keywords

Causality, structure learning, network inference

Learning Prerequisites

Required courses

This course attempts to be as self contained as possible, but it does approach the topic from a quantitative point of view and, as such, students should be comfortable with the basics of (i.e. have taken at least one course in) the following topics before enrolling:

  • Statistics
  • Probability Theory
  • Linear Algebra
  • Calculus (integral and differential)
  • Programing in Pythor and Matlab

As course work will be largely computational, experience with at least one programming language is also required.

 

Important concepts to start the course

Konwlege of probability and calculus as well as programming is a must.

Learning Outcomes

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

  • Identify situations in which a problem/data can be thought of as a network.
  • Analyze data appropriately using a variety of network analytic techniques.
  • Interpret the results of applying network analytics.
  • Propose action based on sound interpretation of network analytics.

Transversal skills

  • Continue to work through difficulties or initial failure to find optimal solutions.
  • Demonstrate the capacity for critical thinking
  • Use both general and domain specific IT resources and tools
  • Access and evaluate appropriate sources of information.

Teaching methods

In class with supporing problem solving sessions.

Expected student activities

TA problem solving sessions, homework, exams, projects

Assessment methods

Regular individual assignments: 30%

Midterm exam: 30%

Final project: 40%

 

Supervision

Office hours Yes
Assistants Yes
Forum No

Resources

Notes/Handbook

course notes

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Causal inference
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
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