Evaluation: 30% classwork (a 10-minute test at every lesson, only 5 best marks will be considered), 30% individual project to be delivered at week 7, 40% final exam.
You can freely use the slides below for your presentations, but I would like to be informed and please acknowledge the source in your presentation. Any comment is welcome.
First lesson (December 19, 2018): introduction to the course, math refresher (gradient, hessian, convex sets and functions), introduction to ML optimization and analysis of stochastic gradient methods (sections 1-4 of Bottou et al)
The individual project is an opportunity for the student to actively use the material taught in the course.
The student is free to choose the goal of its project, but is invited to discuss it with the teacher.
Possible goals are
reproduce an experimental result in a paper,
design or perform an experiment to support/confute a statement in a paper,
apply some of the optimization algorithms described in the course to a specific problem the student is interested in (e.g. for another course, his/her final project, etc.),
compare different algorithms,
implement an algorithm in a distributed system (Spark, TensorFlow, …),
The mark will take into account: originality of the project, presentation quality, technical correctness, and task difficulty. Any form of plagiarism will lead to reduction of the final mark.
A list of possible projects is provided below.
The student will provide
1) a 3-page report formatted according to ICLR template, with unlimited additional pages for bibliography and eventual unlimited appendices to contain proofs, description of code or additional experiments,
2) code developed,
3) a readme file containing instructions to run the code and reproduce the experiments in the report
The report must clearly describe and motivate the goal of the project, provide any relevant background and explain the original contribution of the student.
What is explained in the course can be considered of general knowledge and should not be repeated in the report.
The code must be well commented.
The student will made the material above available online in a zipped folder named with his/her name, and will send the link to the teacher