EE-556 / 6 credits

Teacher: Cevher Volkan

Language: English


Summary

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees, describe scalable solution techniques and algorithms, and illustrate the trade-offs involved.

Content

Learning Prerequisites

Required courses

Previous coursework in calculus, linear algebra, and probability is required. Familiarity with optimization is useful.

Familiarity with python, and basic knowledge of one deep learning framework (Pytorch, TensorFlow, JAX) is needed.

Resources

Moodle Link

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematics of data: from theory to computation
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Practical work: 3 Hour(s) per week x 14 weeks

Reference week

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