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| Semester | spring semester 2019 |
| Course frequency | Every spring sem. |
| Lecturers |
Marcel Lüthi (marcel.luethi@unibas.ch)
Volker Roth (volker.roth@unibas.ch) Thomas Vetter (thomas.vetter@unibas.ch, Assessor) |
| Content | In this seminar we will discuss together the main concepts of reasoning under uncertainty. We will discuss questions such as: How is uncertainty represented and quantified? How can we use probability theory to reason under uncertainty? How can we make good decisions if we have uncertain information? We will also discuss different methods for learning from examples and look at strategies to learn when no examples, but only an assessment of the outcome of a situation is available (reinforcement learning). Finally, we will see these methods in application in the context of natural language processing. Each participant will be assigned a topic, which he/she will summarize and present during the seminar. The topic is studied individually by the student, with the help of a tutor. |
| Learning objectives | At the end of the course, students should have a good overview of different formalisms, methods and strategies to reason about uncertainty. Participants should also have learned to study, summarize and present scientific literature to a peer group in an understandable and instructive way. |
| Bibliography | Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach. |
| Weblink | Webseite Fachbereich Informatik |
| Admission requirements | Open to Master and PhD students with basic knowledge in probability theory and statistics, linear algebra and some first introduction to learning, as for example taught in the course pattern recognition. |
| Course application | https://courses.cs.unibas.ch The number of participants is limited to 20 students. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|
No dates available. Please contact the lecturer.
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Modul: Computer Science Project (Master's degree subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Applications of Machine Intelligence (Master's Studies: Computer Science) Module: Methods of Machine Intelligence (Master's Studies: Computer Science) |
| Assessment format | continuous assessment |
| Assessment details | The final grade of the course is composed of the grade from 4 individual parts: - A presentation about one of the topics discussed in the course book - A written report, summarizing the same topic - A review of a report of another student - An oral exam (31.5. 2018 and 1.6.2018 ) |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |