Deep Learning

Philipp Grohs

March 1st 2017

Organisatorial Stuff...

3h lecture (MSc Level)

Monday 11:30 - 13:00 (seminar room 2nd floor) & Wednesday13:15 - 14:00 (seminar room 2nd floor)

Oral exam at the end of the semester

Exercise classes during the semester

Prerequisites: Linear algebra, Analysis

Desirable: Probability Theory, Functional Analysis, Experiencewith Python or MATLAB

I will create a website at http://mat.univie.ac.at/~grohs/DeepLearningCourse.html.

Face recognition

Face recognition

D. Trump

B. Sanders

B. Johnson

A. Merkel

Describing the content of an image

AI generates sentences describing the content of animage [Vinyals et al., 2015 ]

Go!

AI beats Go-champion Lee Sedol [Silver et al., 2016 ]

Atari games

AI beats professional human Atari-players [Mnih et al., 2015 ]

Rating Attractiveness

Swiss Dating App Blinq (developed in cooperation with ETHZ)

Goal of This Course

How does this work???

Syllabus

1. Machine Learning Basics

2. Neural Networks

3. Convolutional Neural Networks

Caution

This is not a pure Mathematics course!

Some Literature

I. Goodfellow, Y. Bengio and A. Courville. Deep Learning.(2016). Available from http://www.deeplearningbook.org.

F. Cucker and S. Smale. On the Mathematical Foundations ofLearning. Bulletin of the AMS 39/1, pp 1 – 49 (2001).

M. Nielsen. Neural Networks and Deep Learning. Available fromhttp://neuralnetworksanddeeplearning.com.

MATLAB Library MatConvNet, available fromhttp://www.vlfeat.org/matconvnet.

Python Library Theano, available fromhttp://deeplearning.net/software/theano.