Timetable
Description
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

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
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
Lectures, and weekly exercise sessions.
Grading is based on the exam, exercises and group project.
Due to current COVID19 situation general examinations in lecture halls are cancelled. You can check the completion method from the course page or contact the teacher to ask about alternative completion methods.