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.
Separate exams last 3 hours and 30 minutes. Renewal exam (marked with "(U)") is the first separate exam after the course and also a renewal exam of course exam(s). In a renewal exam the points student has earned during the course are taken into account. Exams marked with "(HT)" are allowed only to students who have completed the obligatory projects or other exercises included in those courses. Exams marked with "(HT/U)" are renewals to students who have completed the obligatory projects during the course. Separate exams might cover different area than the lectured course. Check the course web page and contact the responsible teacher if in doubt.