Kaisa_2012_3_photo by Veikko Somerpuro



Here is the course’s teaching schedule. Check the description for possible other schedules.

Mon 9.3.2020
12:15 - 14:00
Wed 11.3.2020
12:15 - 14:00
Mon 16.3.2020
12:15 - 14:00
Wed 18.3.2020
12:15 - 14:00
Mon 23.3.2020
12:15 - 14:00
Wed 25.3.2020
12:15 - 14:00
Mon 30.3.2020
12:15 - 14:00
Wed 1.4.2020
12:15 - 14:00
Mon 6.4.2020
12:15 - 14:00
Wed 8.4.2020
12:15 - 14:00
Mon 20.4.2020
12:15 - 14:00
Wed 22.4.2020
12:15 - 13:00
Mon 27.4.2020
12:15 - 14:00
Wed 29.4.2020
12:15 - 14:00

Other teaching


Prerequisites in terms of knowledge

Multivariate calculus: partial derivatives, gradients, Jacobians. Linear algebra: matrices, eigenvalues, matrix norms. Fundamentals of probability calculus. Basic information theory: cross-entropy. 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


After completing the course, the students should know the general principles of neural networks and deep learning, understand the central methods covered in the course, and be able to apply them to solve real-world problems.
The course will cover, among other things:
  • Background and history of neural networks
  • The backpropagation algorithm
  • Regularization and optimization of neural networks
  • Feed-forward 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 Python-based 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.


All of the following parts of the course have to be completed successfully (above minimum threshold): exam, exercises and group project.