EDAP01 Artificial Intelligence: Course syllabus
Formalities
Course programme: EDAP01 (in Swedish) or EDAP01 (in English). (TFRP20 is identical, but refers to the "individual course", Swedish: fristående kurs, as opposed to LTH's programme courses.)
The course textbook is Artificial Intelligence: A Modern Approach (aka AIMA), 4th ed., by Stuart Russell and Peter Norvig, ISBN-10: 1292401133.
The course is given in English.
Exam will be held on 19th of March 2025. Location and time can be found in the course schedule.
This year's course will be given by Elin A. Topp, Jacek Malec, Pierre Nugues and Stefan Larsson. We will have six TAs (to be confirmed): Faseeh Ahmad, Ayesha Jena, Simon Kristoffersson Lind, Leonard Papenmeier, and Momina Rizwan, and two amanuensis: Ismail Hashim and XXX. Elin A. Topp are officially responsible for the course (kursansvarig). The contact info can be found on each teacher's home page.
The assignments are expected to be filed in via Canvas.
Other matters should be addressed directly to one of the teachers or expedition@cs.lth.se.
In the description below the teachers are denoted by the following acronyms:
- EAT - Elin A. Topp
- JM - Jacek Malec
- PN - Pierre Nugues
- SL - Stefan Larsson
Lectures
Note: The order of the lectures is planned to change!
| Date | No. | Lecture | Who | AIMA chapter |
| 1 | Introduction. Agents. | EAT | 1, 2 | |
| 2 | Search | JM | 3 , 4 | |
| 3 | Advanced search, games | JM | 6 | |
| 4 | Logic, Reasoning | JM | 7, 8.1-8.2 | |
| 5 | Knowledge Representation | JM | 10 | |
| 6 | Probabilistic representation and reasoning | EAT | 12, 13.1-3 | |
| 7 | Probabilistic reasoning over time (HMMs) | EAT | 14 | |
| 8 | Probabilistic Robotics | EAT | 14, 26 | |
| 9 | AI and Robotics @ LU | EAT | 26 | |
| 10 | Machine Learning 1 | PN | 1/2 of 19 | |
| 11 | Machine Learning 2 | PN | 1/2 of 19 and 22 | |
| 12 | Semantic Technology | PN | 1/2 of 10 | |
| 13 | NLP | PN | 24,25 | |
| 14 | Ethics and AI | SL | 28 | |
| 15 | research | TBD | ||
| 19/3 | Exam | See below |
Programming Assignments
There will be three programming assignments. The topics are (probably) search, probabilistic reasoning over time, and machine learning. Every Monday (10-12) and Thursday (8-10) there is scheduled resource time, with TAs available to answer all questions. For details please see the programming assignments page on Canvas.
Reading Advice (2022 version, may get updated)
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 6,
- Logic and Knowledge Representation: Chapter 7.1-7.5, 7.7, 8.1-8.2,
- Knowledge-Based Systems: Chapter 10,
- Probabilistic Knowledge: Chapters 12, 13.1-3, 14
- Introduction to Machine Learning: Chapters 19 without 19.7.5, beginning of Chapter 22 to 22.6 (not included) and without 22.3
- Natural Language Processing, Chapters 24 and 25,
- Robotics, Chapter 26.
- Ethics, Chapter 28.
Just for orientation, here are some pdf files with previous exams: 2005, 2008, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 (and some partial suggestions for its solutions), 2019, 2022 and 2023. Enjoy! The exam is "OPEN BOOK" so it may be worth investing in a hard copy of the textbook.