Nathanaël Perraudin: Catalogue data in Autumn Semester 2023

Name Dr. Nathanaël Perraudin
Name variantsNathanaël Perraudin
N. Perraudin
Address
Swiss Data Science Center (SDSC)
ETH Zürich, OAT W 29
Andreasstrasse 5
8092 Zürich
SWITZERLAND
E-mailnathanael.perraudin@sdsc.ethz.ch
URLhttp://perraudin.info
DepartmentComputer Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
263-3210-00LDeep Learning Information Restricted registration - show details 8 credits3V + 2U + 2AT. Hofmann, N. Perraudin
AbstractDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
ObjectiveIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Prerequisites / NoticeThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Advanced Machine Learning
https://ml2.inf.ethz.ch/courses/aml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2019/CIL/

Introduction to Machine Learning
https://las.inf.ethz.ch/teaching/introml-S19

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/lectures/ss19/comp-stats.php

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f18