English

Every spring semester

To give deeper understanding and skills within areas of machine learning that were introduced in the course EDAN95F / EDAN96 Applied Machine Learning, and to introduced new, advanced, topics that have not been discussed in the course EDAN95F / EDAN96.

The core content of the course consists of four topics: Image classification with deep neural networks (mainly CNNs), text processing with deep recurrent networks and transformers (e.g., RNNs), Bayesian optimisation, and Reinforcement Learning. Other topics will be discussed on overview level. The core topics are handled both theoretically and, through respective assignments, practically.

- show knowledge and understanding within the different topics discussed in the course
- be able to explain the how methods and models that form the course's core in terms of how they are constructed and how they work
- be able to describe other methods and topics that have been discussed in the course on overview level

- be able to handle software tools (introduced in the course) for data handling and pre-processing
- be able to handle tools (introduced in the course) for and perform suitable analysis of methods and models applied in the course
- be able to implement their own models / solutions within the course's core topics

- show understanding for which methods (machine learning models and algorithms) are suitable in which areas of applications / for which type of problem
- show understanding of the limitations that exist in one way or the other for all machine learning approaches, as well as be able to describe these limitations and respective consequences for the application of the models and methods

Lectures

Laboratory exercises

"Laboratory exercises" are programming assignments that can be worked on in small groups and that have to be presented to a TA in respective lab sessions or within a seminar session (entire group of graduate students in the course) led by a teacher

Written assignments

Seminars given by participants

The laboratory sessions (see above) can be replaced with a seminar session for all graduate students on the course, in which the assignments are discussed with a teacher

Failed, pass

EDAN95F / EDAN96 Applied Machine Learning, FMAN45 Machine Learning or comparable background

 

A literature listing is given in the course plan for EDAP30 Advanced Applied Machine Learning

EDA090F

2023-01-24

Maria Sandsten