EE-566 / 4 credits

Teacher: Sayed Ali H.

Language: English


Summary

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

Content

Learning Prerequisites

Recommended courses

Prior exposure to probability theory and linear algebra is recommended.

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Adaptation and Learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Adaptation and Learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Adaptation and Learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Adaptation and Learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 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