This course is designed to prepare Master students for successful research in ML, and prepare PhD students to find new research ideas related to ML theory. Content wise, the technical part will focus on generalization bounds using uniform convergence, and non-parametric regression.
By the end of the course
Learning objectives
acquire enough mathematical background to understand a good fraction of theory papers published in the typical ML venues. For this purpose, students will learn common mathematical techniques from statistics and optimization in the first part of the course and apply this knowledge in the project work
critically examine recently published work in terms of relevance and determine impactful (novel) research problems. This will be an integral part of the project work and involves experimental as well as theoretical questions
find and outline an approach (some subproblem) to prove a conjectured theorem. This will be practiced in lectures / exercise and homeworks and potentially in the final project.
effectively communicate and present the problem motivation, new insights and results to a technical audience. This will be primarily learned via the final presentation and report as well as during peer-grading of peer talks.
Homeworks are designed to
No late homework
Each homework write-up must be neatly typeset as a PDF document using TeX, LaTeX, or similar systems (for more details see below). This is for you to practice getting efficient at it. Ensure that the following appear on the first page of the write-up:
Submit your write-up, one page per question, as a single PDF file by 11:59 PM of the specified due date to gradescope. Follow the instructions and mark the pages that belong to the corresponding questions. See more details on the homework sheet.
Some questions will be graded by the TAs. All questions will be self-graded by you.
Discussions on campuswire
As graduates students we expect you to take this class because you want to learn the material and how to do research. All assessments are designed to maximize the learning effect. Cheating will harm yourself and hence it is of your own interest to adhere to the following policy.
All homework is submitted individually, and must be in your own words.
You may discuss only at a high level with up to two classmates; please list their IDs on the first page of your homework. Everyone must still submit an individual write-up, and yours must be in your own words; indeed, your discussions with classmates should be too high level for it to be possible that they are not in your own words.
We prefer you do not dig around for homework solutions; if you do rely upon external resources, cite them, and still write your solutions in your own words.
When integrity violations are found, they will be submitted to the department’s evaluation board.
Links to books are online resources free from the ETH Zurich network
Learning Theory
Martin Wainwright: High-dimensional statistics (core reference for the course)
Percy Liang: Statistical Learning Theory, Stanford Lecture notes
Some more background reading for your general wisdom, knowledge and entertainment
Keener: Theoretical Statistics: e.g. asymptotic optimality (MLE), UMVU testing
Steinwart and Christmann: Support Vector Machines: more mathematical treatment of RKHS
van der Vaart and Wellner: Weak Convergence and Empirical Processes
We meet in gather.town. Please make sure before the start of the lecture that you can enter gather.town. Some people have had problems with the microphone and camera in the past. The problem sheet for the session are
The virtual interactive session will take place as follows:
Everyone goes into the gather.town main hall where the podium is and the chairs are.
There, the speaker briefly presents the problem and instructions. The problem is usually divided into 3-4 sub-problems. The problem sheet can be found on the website.
Each participant can choose which of the presented sub-problems he or she would like to solve.
For each sub-problem there are two rooms in our meeting room. The goal is for each room to independently solve the corresponding subproblem. Please spread out so that no more than 3-4 students work together in one room.
In each room: 25 minutes of
discussion - you may use the prepared hackmd link or scribbletogether (on iPad/tablet use app) to collaborate (press x to open)
representative prepares a 6 min presentation using hackmd or scribble
One group per question (random choice) will be called to go on stage
Introduce yourself and group members by names
Present your results w/ screenshare (6 min.), take questions (1 min.)
To ask questions please move onto the red carpet in the big hall
You can hear and see only your direct neighbors
If you enter a public space (red carpet and the spot directly behind the podium), everyone can see and hear you
You can move using the arrows on your keyboard
We have created private spaces which you enter when you walk in one of the side rooms. Every group is assigned to one of these rooms where they can discuss and solve the assigned problems.
In each room there are two white bards. If you stand right next to one of these whiteboards, you can click x to open the hackmd respectively scribbles, which you can use to interactively solve the problem
One of the whiteboards contains a scribbles link which you can use as an interactive whiteboard. You can also join the board on your tablet using the 4-digit code (via browser or app, the latter is easier)
The other whiteboard contains a link to a hackmd site where you can jointly write in markdown. For markdown syntax see e.g. this primer. For adding formulas use “$$” and latex syntax. You can also start standard latex environments such as ‘\begin{align}’
You can look here to familiarize yourself with the gather.town environment.
For completeness, the links for collaboration for each of the individual rooms are:
Question 1: Group A Hackmd Scribble (Code: 2Q9T); Group B Hackmd Scribble (Code: GHE9)
Question 2: Group A Hackmd Scribble (Code: 2D3W); Group B Hackmd Scribble (Code: 5RL6)
Question 3: Group A Hackmd Scribble (Code: A9CH); Group B Hackmd Scribble (Code: YWFH)
Question 4: Group A Hackmd Scribble (Code: RYMD); Group B Hackmd Scribble (Code: 2FEZ)
