Self-study - Learning and Knowledge

This collection of self-study material will give you a very brief introduction to some fundamental concepts and methods of machine learning, knowledge representations and reasoning. The emphasis will be on ML. We will not have time to dig deep and this is an area where you learn by doing.

Our ambition is that all of you should get some hands-on experience from using some important toolboxes on real data, and hopefully be encouraged to further studies during your PhD career. Many of you probably know some of the material already and can scan through it quickly; in this case we encourage you to use this spare time on the collection of additional material, such as the very many good online courses, to participate in a Kaggle competition, or do a extended research project as (part of) your examination. The flexibility we offer is described on the examination page.

The examination tasks on Learning and Knowledge

In the table below we provide an estimate for a target time for the different self-study parts. This is obviously very hard to estimate given how different your backgrounds are. If you end up spending much time on a topic, you should probably ask for support. Use the forum to ask questions, talk to your colleagues and use the local sessions. When you have spent significantly more effective time than what is estimated below, say double, we suggest that you stop and make sure to ask for help to not spend too much time. The time is defined based on the mandatory part of the material.

In the last column we have links to pages that you can edit and share your knowledge in the corresponding area. Maybe you have some cool examples you would like others to test? Some videos that they must see?

We want you to work in study groups whenever possible so that you practice communicating with others, form the network within WASP and help each other. Ideally these study groups are interdisciplinary.

Material marked in red in the pages linked to below is optional.

Material to study	Examination	Time*	Q&A	Own contributions
Tools installation		2h	Forum	Own contrib.
Supervised Learning	Music classification Assignment_M1A1	15h	Forum	Own contrib.
Unsupervised Learning		4h	Forum	Own contrib.
Deep Learning and NLP	DL for NLP Assignment_M1A2	15h	Forum	Own contrib.
Knowledge Representation and Reasoning	See Assignment_M1A3	4h	Forum	Own contrib.
Additional Topics	Group Study Assignment_M1A3	14h	Forum	Own contrib.
Perspectives and Further Resources		0h	Forum	Own contrib.

* Total time for self-study + examination (excluding the optional material, the local sessions (10h) and the two-day session (16h))