Interactive Syllabus

Fundamentals of Artificial Intelligence

Info

Homework
Course-related instructions: PDF
Course syllabus at IS MU
Study materials
- All learning materials with slides
- Video recordings
- Sample exams from last years: I., II.
- Last year's slides (in Czech): PDF handout and for presentation
  - also available to all students who have taken the course in the past
- Textbooks and internet resources
  - Russell, S., Norvig, P. Artificial Intelligence: A Modern Approach, 4th edition. Pearson, 2020.
    Available at MU electronically via e-loan
  - Online course, Intro to Artificial Intelligence, Peter Norvig, Sebastian Thrun.
  - Search and metaheuristics
    - El-Ghazali Talbi, Metaheuristics: From Design to Implementation. Wiley, 2009. PDF
    - Holger H. Hoos und Thomas Stützle, Stochastic Local Search. Morgan Kaufmann Publishers, 2005.
      http://iridia.ulb.ac.be/~stuetzle/Teaching/HO14/
    - Metaheuristic optimization frameworks: a survey and benchmarking, Soft Computing, 2012, Volume 16, Issue 3, pp 527-561.
      http://link.springer.com/article/10.1007%2Fs00500-011-0754-8
    - FI:PA184 Heuristic Methods for Search and Optimization (Spring 2010)
      https://is.muni.cz/auth/predmet/fi/jaro2010/PA184
  - Planning
    - Roman Barták, Planning and scheduling course at MFF UK,
      http://ktiml.mff.cuni.cz/~bartak/planovani/index.html
    - M. Ghallab, D. Nau, P. Traverso, Automated Planning and Acting. Cambridge University Press, 2016.
      http://projects.laas.fr/planning/
    - M. Ghallab, D. Nau, P. Traverso, Automated Planning: Theory and Practice. Morgan Kaufmann, 2004. (older edition of the previous book)
      http://projects.laas.fr/aptp/
  - Uncertainty
    - Roman Barták, Artificial intelligence II course at MFF UK,
      http://ktiml.mff.cuni.cz/~bartak/ui2/index.html
  - Robotics
    - Steven M. LaValle, Planning algorithms, Cambridge University Press, 2006.
      http://planning.cs.uiuc.edu/ with books source in PDF
Questions and consultation: discuss the date by e-mail with the teacher Hana Rudová

Introduction to Artificial Intelligence. Search and metaheuristics: concepts, introduction to single-solution based metaheuristics.

Open

Single-solution based metaheuristics.

Open

Population-based metaheuristics.

Open

Formalization of planning problems, representations. State space planning: forward search.

Open

State space planning: backward search. Probability theory (short revision). Bayesian networks.

Open

Exact and approximate inference. Time and uncertainty.

Open

Test in a written form (10.11.2022)

Utility theory, decision networks. Sequential decision problems.

Open

Value and policy iteration. Robots, robotic perception. Planning: configuration space.

Open

Robotics: motion planning and control

Open

Exercises with solutions

Open

Acknowledgements

Course materials are based on the following sources:

Roman Barták, Planning and scheduling course at MFF UK
http://ktiml.mff.cuni.cz/~bartak/planovani/index.html
Roman Barták, Artificial intelligence II course at MFF UK
http://ktiml.mff.cuni.cz/~bartak/ui2/index.html
Roman Barták, Introduction to Artificial Intelligence course at MFF UK, http://ktiml.mff.cuni.cz/~bartak/ui_intro/index.html
El-Ghazali Talbi, Metaheuristics: From Design to Implementation. Wiley, 2009.
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition. Prentice Hall, 2020
http://aima.cs.berkeley.edu/, available at MU elektronically via e-loan

Fundamentals of Artificial Intelligence
- Study now
  
  Slides from all lectures in one file
- Study now
  
  Introduction to Artificial Intelligence. Search and metaheuristics: concepts, introduction to single-solution based metaheuristics.
- Study now
  
  Single-solution based metaheuristics.
- Study now
  
  Population-based metaheuristics.
- Study now
  
  Formalization of planning problems, representations. State space planning: forward search.
- Study now
  
  State space planning: backward search. Probability theory (short revision). Bayesian networks.
- Study now
  
  Exact and approximate inference. Time and uncertainty.
- Study now
  
  Test in a written form (10.11.2022)
- Study now
  
  Utility theory, decision networks. Sequential decision problems.
- Study now
  
  Value and policy iteration. Robots, robotic perception. Planning: configuration space.
- Study now
  
  Robotics: motion planning and control
- Study now
  
  Exercises with solutions

Operations

View all