The course textbook is Artificial Intelligence: A Modern Approach, 3/e, by Stuart Russell and Peter Norvig, ISBN-10: 0132071487 or 1292153962.
The course is given in English.
Exams will be held on 20th of March 2019 and 29th of August 2019. Location and times can be found in the course schedule.
This year's course will be given by Jacek Malec, Elin Anna Topp, Pierre Nugues and Stefan Larsson. We will have two TAs: Erik Gärtner and Matthias Mayr. Jacek Malec is officially responsible for the course (kursansvarig). The contact info can be found on each teacher's home page. The mail address for filing in assignments is email@example.com. Other matters should be addressed directly to one of the teachers or firstname.lastname@example.org.
In the description below the teachers are denoted by the following acronyms:
- EAT - Elin Anna Topp
- JM - Jacek Malec
- PN - Pierre Nugues
- SL - Stefan Larsson
|23/1||1||Introduction. Agents.||JM||1, 2|
|30/1||3||Games, CSP||JM||5, 6|
|1/2||4||Probabilistic representation and reasoning||EAT||13, 14||A1 announced|
|6/2||5||Probabilistic reasoning over time (HMMs)||EAT||15|
|8/2||6||Logic, Reasoning||JM||7, 8||A2 announced|
|13/2||7||Machine Learning 1||PN||1/2 of 18|
|15/2||8||Machine Learning 2||PN||1/2 of 18||A1 deadline, A3 announced|
|20/2||9||Knowledge Representation||JM||9, 12|
|22/2||10||Planning||JM||10, 11||A2 deadline|
|1/3||12||Semantic Technology||PN||12||A3 deadline|
|6/3||13||Probabilistic Robotics||EAT||15, 25|
|11/3||15||Ethics and AI||SL|
|20/3||Exam||All :-)||8-13, MA10 E-H|
|29/8||Re-exam||All :-)||8-13, Sparta C|
There will be three programming assignments, to be done in pairs. The topics are (probably) search, machine learning, and hidden Markov models (probabilistic reasoning over time). For details please see the programming assignments page. The announcement dates above are tentative.
Below you will find a list of the chapters in the textbook, and also other material, that we expect you to get acquainted with before the exam.
Besides, some sections may get excluded from the list below. Some additional material from the teachers may get added to the list.
- Introduction: Chapters 1 and 2,
- Search: Chapter 3, 4 and 5,
- Constraint Satisfaction: Chapter 6.1-6.3,
- Logic and Knowledge Representation: Chapter 7 and 8.1,
- Planning: Chapter 10.1, 10.2,
- Knowledge-Based Systems: Chapter 12.1-12.3, 12.5,
- Probabilistic Knowledge: Chapters 13, 14, 15 and 20,
- Introduction to Machine Learning: Chapter 18,
- Natural Language Processing, Chapters 22 and 23,
- Robotics, Chapter 25.
Just for orientation, here are some pdf files with 2005, 2008, 2011, 2012, 2013, 2014, 2015, 2016, 2017 and 2018 (and some partial suggestions for its solutions) exam questions. Enjoy! The exam is "OPEN BOOK" so it may be worth investing in a hard copy of the textbook.