TDDC17 Artificial Intelligence

Lectures

2024 Fall Lectures!

The schedule and content for the 2024 Fall Lectures is updated.

2024 slides will be posted incrementally, before each lecture, old slides still available.

Last Changed: 11 Oktober, 2024 12:55

Week 36

• Tue. 3/9 15-17 C1
  • Lecture 1: Course Introduction, History of AI (Lecturer: Fredrik Heintz)
  • Hour 1: What is AI? Philosophical and mathematical roots.
  • Hour 2: The Intelligent Agent Paradigm.
  • Reading: ch1, ch2, ch27[27.1-2]. Turing article.
  • Slides: Le1 (2024)
• Thu. 5/9 08-10 C1
  • Lecture 2: Search I (Lecturer: Fredrik Heintz)
  • Hour 1: The physical symbol system hypothesis. Introduction to search.
  • Hour 2: Uninformed search.
  • Reading: ch3:[3.1-3.4], Newell and Simon article.
  • Slides: Le2 (2024)

Week 37

• Tue. 10/9 13-15 C4
  • Lecture 3: Constraint Satisfaction (Lecturer: Jendrik Seipp)
  • Constraint Satisfaction Problems (2024)
  • Backtracking and Inference (2024)
  • Reading: ch5: 5.1-5.4
• Thu. 12/9 8-10 C4
  • Lecture 4: Search II (Lecturer: Fredrik Heintz)
  • Hour 1: Informed Search
  • Hour 2: Beyond Classical Search, Adverserial Search
  • Reading: ch3:[3.5-3.6], ch4:[4.1], ch6: [6.1-6.4]
  • Slides: Le3 (2024)
• Fri. 13/9 15-17 C4
  • Lecture 5: Knowledge Representation I (Lecturer: Jendrik Seipp)
  • Propositional Logic: Syntax, Semantics, Conjunctive Normal Form
  • Reading: ch7 (7.7.3-4 not required)

Week 38

• Mon. 16/9 10-12 C1
  • Lecture 6: Knowledge Representation II (Lecturer: Jendrik Seipp)
  • Propositional Logic: Resolution, DPLL
  • Propositional Logic: Local Search
  • Reading: ch7: 7.6, ch8: 8.1-2:
• Tue. 17/9 13-15 C1
  • Lecture 7: Knowledge Representation III (Lecturer: Jendrik Seipp)
  • First-Order Logic
  • Answer Set Programming
  • Reading: ch9: 9.1, 9.3.1, 9.3.2, 9.4.2, 9.4.3,9.4.4, ch10: 10.6.1
• Fri. 20/9 15-17 C1
  • Lecture 8: Bayesian Networks (Lecturer: Jendrik Seipp)
  • Probability
  • Bayesian Networks
  • Reading: ch12, ch13: 13.1, 13.2.1

Week 39

• Mon. 18/9 10-12 C1
  • Lecture 9: Machine Learning I (Lecturer: Fredrik Heintz)
  • Hour 1: Introduction to Machine Learning
  • Hour 2: Supervised Learning
  • Reading: ch19: 19.1-19.4, 19.6, ch21: 21.1
  • Slides: Le9 (2024)
• Tue. 24/9 15-17 C1
  • Lecture 10: Machine Learning II (Lecturer: Fredrik Heintz)
  • Hour 1: Neural Networks
  • Hour 2: Deep Generative Models
  • Reading: ch22: 22.1-22.4.1, 22.6
  • Slides: Le10 (2024)
• Fri. 27/9 15-17 C1
  • Lecture 11: Machine Learning III (Lecturer: Fredrik Heintz)
  • Reinforcement Learning
  • Reading: ch16: 16.1-2, ch23: 23.1-23.4.3
  • Slides: Le11 (2024)

Week 40

• Mon. 30/9 10-12 C1
  • Lecture 12: Planning 1 (Lecturer: Jendrik Seipp)
  • Planning Tasks
  • Abstraction Heuristics
  • Reading: ch11: 11.1, 11.2.1, 11.3.2
• Tue. 1/10 13-15 C1
  • Lecture 13: Planning 2 (Lecturer: Jendrik Seipp)
  • Delete Relaxation
  • Delete Relaxation Heuristics
• Fri. 4/10 15-17 C4 (OBS!)
  • Lecture 14: Planning 3 (Lecturer: Jendrik Seipp)
  • Markov Decision Processes
  • Solving MDPs
  • Reading: ch16: 16.1, 16.2

Week 41

• Mon. 7/10 10-12 C1
  • Lecture 15: Robotics/Perception I (Lecturer: Piotr Rudol)
  • Reading: Chapt. 25
  • Slides: Le15
• Tue. 8/10 13-15 C1
  • Lecture 16: Robotics/Perception II (Lecturer: Mariusz Wzorek)
  • Reading: Chap 26
  • Slides: Le16
• Fri. 11/10 15-17 C1
  • Lecture 17: Course Summary and Discussion (Lecturer: Fredrik Heintz)
  • Slides: Le17 (2024)

Page responsible: Fredrik Heintz
Last updated: 2024-10-11