Instruction
Name  Cr  Method of study  Time  Location  Organiser 

Deep Learning (HT)  5 Cr  General Examination  6.2.2020  6.2.2020  
Deep Learning  5 Cr  Lecture Course  9.3.2020  29.4.2020 
Name  Cr  Method of study  Time  Location  Organiser 

Deep Learning (HT)  5 Cr  General Examination  5.9.2019  5.9.2019  
Deep Learning (HT)  5 Cr  General Examination  12.6.2019  12.6.2019  
Deep Learning (HT)  5 Cr  General Examination  10.4.2019  10.4.2019  
Deep Learning (HT/U)  5 Cr  General Examination  7.2.2019  7.2.2019  
Deep Learning  5 Cr  Examination  20.12.2018  20.12.2018  
Deep Learning  5 Cr  Lecture Course  29.10.2018  14.12.2018  
Deep Learning (HT)  5 Cr  General Examination  6.9.2018  6.9.2018  
Deep Learning (HT/U)  5 Cr  General Examination  6.4.2018  6.4.2018  
Deep Learning (HT/U)  5 Cr  General Examination  2.2.2018  2.2.2018  
Deep Learning  5 Cr  Lecture Course  1.11.2017  15.12.2017 
Prerequisites
Prerequisites in terms of knowledge
Multivariate calculus: partial derivatives, gradients, Jacobians. Linear algebra: matrices, eigenvalues, matrix norms. Fundamentals of probability calculus. Basic information theory: crossentropy. Machine learning: good grasp of the process of building models, training, testing / evaluating performance.
Prerequisites for students in the Data Science programme, in terms of courses
DATA11002 Introduction to Machine Learning
Prerequisites for other students in terms of courses
DATA11002 Introduction to Machine Learning
Recommended preceding courses
None
Learning outcomes
Contents

Background and history of neural networks

The backpropagation algorithm

Regularization and optimization of neural networks

Feedforward neural networks

Convolutional neural networks

Recurrent neural networks

Various advanced topics in brief: GANs, autoencoders and deep generative models

Practical vision and natural language applications with Pythonbased deep learning frameworks
Activities and teaching methods in support of learning
Lectures, and weekly exercise sessions.
Study materials
Lecture slides and computer exercise materials will be provided during the course.
Course book: Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press 2016. Online version: http://www.deeplearningbook.org
Assessment practices and criteria
Grading is based on the exam, exercises and group project.