Optimization for Learning

Optimization for Learning

Course program

The course program contains the schedule and relevant information regarding lectures, exercises, deadlines etc. All relevant information will also be posted on this canvas page.


We will follow the department's general Covid-19 teaching policy. Lectures will be online and we will have two online and two on-site exercise sessions per week. See the course program for more info.

Before the course

Mathematical prerequisites. The course is fairly mathematical. We will in the teaching assume that you feel reasonably comfortable with the content of this mathematical prerequisites document.

Coding environments. We will program in Julia and in Python.

Weekly schedule

Lecture videos

Some lecture videos (that are short flipped classroom type videos, not lecture recordings) can be found here. The videos are intended for active listening. This means pausing, skipping 15 s back (or forward) within video to repeat, e.g., an argument, skipping between videos to recall concepts, changing playback speed, and maybe taking notes and verifying calculations while watching.

Lecture videos for the remaining lectures are recorded during live Zoom presentations and can be found here.

Lecture slides

All course slides in one pdf

Extra material (not in course this year)

Lecture recordings

Recordings of some discussion sessions:

  • DS2 - convex functions, subdifferentials

Recording of algorithm overview lecture (not part of course this year)

Exercise material

Suggested exercises

Session Exercise set
Before Mathematical prerequisites
E1-E2 Introduction to Julia and Ch 1: 1-9, 12-20
E3-E4 Ch 1: 21-26, 31-32, 36, Ch 2: 1-6, 15-16, 8-10
E5-E6 Ch 2: 11-13, Ch 3: 1-2, 4-7, 10-15, 17
E7-E8 Ch 3: 16, 18-21 Ch 4: 1-9
E9-E10 Introduction to Python and Ch 5: 1-9
E11-E12 Ch 6: 1-10
E13-E14 Ch 7: 1-7


If your submission is not passed: Two resubmissions are allowed on the first assignment. Only one resubmission is allowed on the second assignment. The resubmission deadlines (one week after we are done grading the particular assignment) will be posted as notifications here in Canvas.


Contact information

Mika Nishimura Ladok administrator mika.nishimura@control.lth.se
  Pontus Giselsson Course responsible pontusg@control.lth.se
Manu Upadhyaya Teaching assistant manu.upadhyaya@control.lth.se
Hamed Sadeghi Teaching assistant hamed.sadeghi@control.lth.se

Course representatives: Henrik Paldan, Andre Rath

Additional material