Preliminary version of syllabus

Course syllabus

Optimering för maskininlärning
Optimization for Learning

FRTN50, 7,5 credits, A (Second Cycle)

Valid for: 2019/20
Decided by: PLED F/Pi
Date of Decision: 2019-03-26

General Information

Elective for: D5-mai, E4, F5, F5-r, F5-mai, I4, M4, Pi5-ssr
Language of instruction: The course will be given in English

Aim

Learning from data is becoming increasingly important in many different engineering fields. Models for learning often rely heavily on optimization; training a machine is often equivalent solving a specific optimization problem. These problems are typically of large-scale. In this course, we will learn how to solve such problems efficiently. The large-scale nature of the problems renders traditional methods inapplicable. We will provide a unified view of algorithms for large-scale convex optimization and treat algorithms for the nonconvex problem of training deep neural networks.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must

Contents

The course has lectures, exercises, and four hand-in assigments.

The lectures will cover:

convexity, models for learning, unified convex optimization algorithm view, fixed-point iterations, monotone operators, nonexpansive mappings, stochastic methods, reduced variance methods, block-coordinate methods, nonconvex stochastic gradient descent and variations for for deep learning training.

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam (5 hours), 4 hand-in exercises. In case of less than 5 registered students, the exam may be given in oral form.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Parts
Code: 0119. Name: Exam.
Credits: 7,5. Grading scale: TH.
Code: 0219. Name: Hand-in 1.
Credits: 0. Grading scale: UG.
Code: 0319. Name: Hand-in 2.
Credits: 0. Grading scale: UG.
Code: 0419. Name: Hand-in 3.
Credits: 0. Grading scale: UG.
Code: 0519. Name: Hand-in 4.
Credits: 0. Grading scale: UG.

Admission

Required prior knowledge: FMAN60 Optimization
The number of participants is limited to: No
The course might be cancelled: If the number of applicants is less than 12.

Reading list

Contact and other information

Course coordinator: Pontus Giselsson, pontusg@control.lth.se
Director of studies: Anton Cervin, anton.cervin@control.lth.se
Course homepage: http://www.control.lth.se/course/FRTN50