263-3210-00L  Deep Learning

SemesterAutumn Semester 2017
LecturersT. Hofmann
Periodicityyearly course
Language of instructionEnglish
CommentNumber of participants limited to 300.


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 conditions:
1) The number of participants is limited to 300 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Machine Learning
https://ml2.inf.ethz.ch/courses/ml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2017/CIL/

Learning and Intelligent Systems
https://las.inf.ethz.ch/teaching/lis-s17

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

Computational Statistics
https://stat.ethz.ch/education/semesters/ss2012/CompStat/sk.pdf

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f16

Data Mining: Learning from Large Data Sets
https://las.inf.ethz.ch/teaching/dm-f16