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 at IS MU
- Sample exams from last years: I., II.
- Last year's slides in PDF: handout and for presentation
  - also available to all students who have taken the course in the past
  - slides in Czech from 2021: PDF handout and for presentation
- Textbooks and Internet resources
  - Russell, S., Norvig, P. Artificial Intelligence: A Modern Approach, 4th edition. Pearson, 2020.
    Available at MU electronically via e-loan. See also the book's exercises
  - 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/s00500-011-0754-8
  - 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: send an e-mail to the teacher, Hana Rudová

Slides in one file will be published at the end of the semester

The outline below is preliminary. Slides will be added during the semester

Introduction to artificial intelligence. Search and metaheuristics: concepts, introduction to single-solution based metaheuristics.

Open

Single-solution based metaheuristics.

Open

Population-based metaheuristics.

Open

Programming homework: specification October 13, deadline October 26

Introduction to planning, planning problem representation.

Open

State space planning: forward search, backward search

Open

Voluntary: Plan space planning (partial order planning).

Open

Probability theory (short revision). Bayesian networks. Exact and approximate inference.

Open

Probabilistic reasoning over time. Utility theory.

Open

Midterm test: November 24

Decision making. Value iteration.

Open

Policy iteration. Robots, robotic perception. Planning: configuration space.

Open

Robotics: motion planning and control

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 electronically 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
  
  Introduction to planning, planning problem representation.
- Study now
  
  State space planning: forward search, backward search
- Study now
  
  Voluntary: Plan space planning (partial order planning).
- Study now
  
  Probability theory (short revision). Bayesian networks. Exact and approximate inference.
- Study now
  
  Probabilistic reasoning over time. Utility theory.
- Study now
  
  Decision making. Value iteration.
- Study now
  
  Policy iteration. Robots, robotic perception. Planning: configuration space.
- Study now
  
  Robotics: motion planning and control

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