Thank you to Professors Sonja Buchegger, Juan Carlos Nieves, Johan Eker and PhD student Albin Heimersson for contributions for this task.

In this assignment you will work in groups. The idea is to explore the breadth of the area. A number of topic are listed below. Your task is to study these in the group and produce a number of deliverables (see further down).

Topics

Gaussian processes
Reinforcement learning
- Describe problem type
- Traditional vs deep RL
- Give examples of where it works well and what is challenging
Knowledge representations and reasoning (KRR)

What is a knowledge base?
What are the factors that determine which information can be expressed in a given specification language? Give examples.
What is the cost of having a high expressive specification language? Give examples.
What is a reasoning process? Give examples of reasoning processes.
Can learning methods and KRR methods be complemented by each other? If so, give examples.

ML and Security, Privacy, FAT (Fairness, Accountability, Transparency), 6 topics to pick from, grouped by their relation to ML. You only have to pick one topic for the group.

Machine learning for security (ML and Security)
* What kinds of security problems can/cannot be helped by ML?
* Provide an example for security by ML to explain what is done.
* What are potential problems?
Adversarial machine learning (ML and Security)
* What kinds of attacks are there?
* Give an example to show what is being attacked and how.
* What countermeasures exist?
* What is the difference to GAN (Generative Adversarial Networks)?
Privacy-preserving machine learning (ML and Privacy)
* What are the main privacy concerns when it comes to machine learning?* Give an example to illustrate the problem
* What are potential consequences of privacy breaches?
* Pick a proposed solution (for example, using cryptography and secure multi-party computation), explain how it works and its limitations.
Differential Privacy (ML and Privacy)
* What problem does it address?
* How does it work?
* Give an example to show how data is treated.
* What are the costs and trade-offs when it comes to machine learning?
Data anonymization/re-identification (ML and Privacy)
* What does k-anonymity mean?
* What problem does it address?
* Give an example to show how data can be transformed to fulfill k-anonymity.
* What concerns are addressed by l-diversity and t-closeness?
* What are the effects on machine learning?
FAT ML (fairness, accountability, transparency for machine learning)
* What are the FAT concerns when it comes to machine learning?
* Provide an example
* What are some of the proposed countermeasures?

Infrastructure for computing
- Try out and compare infrastructures for computing. Compare speed, data storage, documentation, setup time, flexibility, ease of use...
  - Snic.se (apply here https://www.snic.se/allocations/small-allocations/#anchor-630452)
  - At RISE SICS North, they have a platform called Hops with 24 Nvidia GPUs. They give free access to academic researchers in Sweden www.hops.site
  - Ericsson research data center. Tutorial instructions on how to get started. Emails with login credentials should have been sent to you (you can change password at auth.erdc.ericsson.net)
  - Google colab
  - Aws (use free trial)
  - ...

Deliverables

For each topic
- A presentation for each of the above topics that covers, as a minimum, the things mentioned under each topic. The presentation should include pointers to the sources of information that you used.
  - When possible the presentation should include a small demo of the main concepts, tools, etc in this topic
  - The presentation can have two parts, one that covers the basic material that you believe that everyone should know and a more advanced section.
- A set of questions for each topic that captures the most important aspects of the topic. The questions can be both multiple-choice, free text and calculations.
Document that states how the work was distributed with an explicit mentioning of who prepared what topic and signed by all that the group believes has contributed fairly to the work.

Suggested way to work

Given that the number of topics is large you are probably best off to distribute the topics between you within the group. You would then prepare the presentations of the topics individually or in pairs and then present this to the other members of the group. This would allow them to give feedback on the presentation at the same time as they get to know the material so that in the end everyone has reach the goal of broad knowledge.

At the examination session

For each topic
- a random person makes the presentation, i.e. everyone in the group should be able to deliver the presentations. For presentations with a basic and an advanced section, it is enough to be able to present the basic section. During the examination session the person preparing the material would then take over to deliver the more advanced parts.
- a discussion follows with the other groups including looking at the questions prepared. Was the most important aspects covered in the presentation? Did you cover the same aspects in the presentations and questions?