Artificial Intelligence Prof. Dr. Jana Koehler, Dr. Álvaro Torralba

Organization

Successful participation in the course yields 9 ECTS. The course consists of oral lectures, 2*90 minutes per week, as well as exercises that will be supervised in tutorial groups (90 minutes/week). All lectures and tutorials will be held in English.

The lectures will normally be held Mondays 14:15--15:45 in Lecture Hall I (E2 5) and Tuesdays 16:15--17:45, in Guenter-Hotz-Hoersaal (E2 2). There will be some exceptions, in day of the week, time, and/or place. Exact details will be made available in the "Course Calendar" below.

Registration for the course/exams is in HISPOS as usual (contact Evelyn Kraska in case of questions). If you have difficulties registering, or wish to change tutorials, please send an email to Daniel Gnad. Note that, for changing the tutorial group, you need a switching partner. You can try to find a switching partner via the "Offtopic" Forum.


Exercises. There will be two kinds of exercises, paper exercises and practical exercises. Each of the two kinds of exercises will be counted separately; each will have a seprate series of exercise sheets, and will have a separate maximum amount of points possible. To qualify for the exam, you need to obtain at least 50 points in each, paper exercises and practical exercises. (If you want to participate in future editions of this course, then you need to qualify anew; see also below.)

The paper exercises will involve applying the introduced concepts and algorithms to examples, and leading simple proofs. The paper exercise sheets will be handed out and submitted in a weekly cycle, i.e., in 1-week intervals. The submission deadline will be stated on each sheet. 

The practical exercises will consist of experimentation with off-the-shelf AI problem-solving formalisms and tools. On each exercise sheet, you will be given an example problem, and will be asked to model that problem in a particular formalism, and solve your model with an off-the-shelf tool. Your model will be checked manually by the tutors, and will be graded based on its correctness. The practical exercise sheets will be handed out and submitted in 2-week intervals. The submission deadline will be stated on each sheet.

Students can form groups of up to 3 authors for the exercise solutions. All group members must be registered into the same tutorial group. Every student can be member of at most one group, i.e., the same group must address both the paper and the practical exercises.


Exam and final grade. There will be a written exam at the end of the course. The final grade will be determined based on the performance in the exam. A re-exam will be held beginning October.

Each exam counts as a separate attempt to pass the course. The re-exam is an opportunity to improve your grade, i.e., the better one of the two grades will count for your curriculum.

ATTENTION! The re-exam is your only chance to improve your grade.

  • By the study regulations, if you do not pass this edition of the course (if you fail, or are absent, in both the exam and the re-exam), then you can participate in future editions of the course as additional attempts to pass the course. (In this case, you need to qualify anew for the exam of that edition of the course.)
  • If you do pass this edition of the course, then you cannot improve your grade anymore. The only exception to the latter is if you already pass the exam: in that case, you can try to improve your grade in the re-exam.

Course Material. For most lectures, there will be two kinds of slides, pre-handouts and post-handouts. Pre-handouts do not contain the answers to questions asked during the lecture sessions, and do not contain the details for examples worked during the lecture sessions. The post-handouts do contain all this, and correct any bugs. The pre-handouts are made available one day before the lecture sessions on each chapter, the post-handouts are made available directly after the lecture sessions on a chapter are finished.

Most of the course follows the standard AI text book by Russel and Norvig (RN). HOWEVER, several chapters do NOT follow that book one-to-one, and some do not follow it at all. The ground truth throughout the course are the results as stated in the post-handouts. A few details about the relevant chapters of RN are given in the table at the end of this page, as well as at the end of each topic in the post-handouts.

 


Date Place Lecturer Chapter(s) / Tutorials Exercise Deadlines Material
Mon 08.04 Lecture Hall I in E2 5 Koehler About this course; Introduction to AI   Russel/Norvig Chapter 1
Tue 09.04 Günter-Hotz Koehler Paradigms in AI Research   Russel/Norvig Chapter 2 (Loosely followed)
Mon 15.04 Lecture Hall I in E2 5 Koehler Intelligent Agents   Russel/Norvig Chapter 2 (Loosely followed)
Tue 16.04 Günter-Hotz Koehler Classical Search, Part I: Basics, and Blind Search   Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Mon 22.04     Osterferien    
Tue 23.04 Günter-Hotz Koehler Classical Search, Part I: Basics, and Blind Search ; Classical Search, Part II: Informed Search Paper Sheet 1 OUT Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Mon 29.04 Lecture Hall I in E2 5 Koehler Classical Search, Part II: Informed Search   Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Tue 30.04 Günter-Hotz Koehler Propositional Reasoning, Part I: Principles Paper Sheet 1 IN; Sheet 2 OUT
Sheet 1 OUT
Russel/Norvig Chapter 7 (Loosely followed)
Mon 06.05 Lecture Hall I in E2 5 Koehler Propositional Reasoning, Part I: Principles ; Propositional Reasoning, Part II: SAT Solvers   Russel/Norvig Chapter 7 (Loosely followed)
Tue 07.05 Günter-Hotz Koehler Propositional Reasoning, Part II: SAT Solvers Paper Sheet 2 IN; Sheet 3 OUT Russel/Norvig Chapter 7 (Loosely followed)
Mon 13.05 Lecture Hall I in E2 5 Torralba Predicate Logic Reasoning, Part I: Basics   Russel/Norvig Chapter 8 (Loosely followed)
Tue 14.05 Günter-Hotz Torralba Predicate Logic Reasoning, Part I: Basics ; Predicate Logic Reasoning, Part II: Reasoning Paper Sheet 3 IN; Sheet 4 OUT
Sheet 1 IN; Sheet 2 OUT
Russel/Norvig Chapter 8 (Loosely followed)
Mon 20.05 Lecture Hall I in E2 5 Torralba Predicate Logic Reasoning, Part II: Reasoning   Russel/Norvig Chapter 9 (Loosely followed)
Tue 21.05 Günter-Hotz Torralba Constraint Satisfaction Problems, Part I: Basics, and Naive Search ; Constraint Satisfaction Problems, Part II: Inference, and Decomposition Methods Paper Sheet 4 IN; Sheet 5 OUT Russel/Norvig Chapter 6 (Loosely followed)
Mon 27.05 Lecture Hall I in E2 5 Torralba Constraint Satisfaction Problems, Part II: Inference, and Decomposition Methods   Russel/Norvig Chapter 6 (Loosely followed)
Tue 28.05 Günter-Hotz Torralba Adversarial Search Paper Sheet 5 IN; Sheet 6 OUT
Sheet 2 IN Sheet 3 OUT
Russel/Norvig Chapter 5 (Loosely followed)
Mon 03.06 Lecture Hall I in E2 5 Torralba Adversarial Search; General Game Playing    
Tue 04.06 Günter-Hotz Torralba Planning, Part I: Framework   Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Mon 10.06     Pfingstmontag    
Tue 11.06 Günter-Hotz Torralba Planning, Part I: Framework ; Planning, Part II: Algorithms Paper Sheet 6 IN; Sheet 7 OUT
Sheet 3 IN Sheet 4 OUT
Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Mon 17.06 Lecture Hall I in E2 5 Torralba Planning, Part II: Algorithms   Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Tue 18.06 Günter-Hotz Torralba Planning, Part II: Algorithms Paper Sheet 7 IN; Sheet 8 OUT Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Mon 24.06 Lecture Hall I in E2 5 Torralba Probabilistic Reasoning I   Russel/Norvig Chapter 14 (Loosely followed)
Tue 25.06 Günter-Hotz Torralba Probabilistic Reasoning I ; Probabilistic Reasoning II Paper Sheet 8 IN; Sheet 9 OUT
Sheet 4 IN; Sheet 5 OUT
Russel/Norvig Chapter 14 (Loosely followed)
Mon 01.07 Lecture Hall I in E2 5 Torralba Probabilistic Reasoning II   Russel/Norvig Chapter 14 (Loosely followed)
Tue 02.07 Günter-Hotz Koehler Knowledge Representation I Paper Sheet 9 IN; Sheet 10 OUT Russel/Norvig Chapter 12 (Loosely followed)
Mon 08.07 Lecture Hall I in E2 5 Koehler Knowledge Representation II   Russel/Norvig Chapter 12 (Loosely followed)
Tue 09.07 Günter-Hotz Koehler Machine Learning Basics I Paper Sheet 10 IN
Sheet 5 IN
 
Mon 15.07 Lecture Hall I in E2 5 Koehler Machine Learning Basics II    
Tue 16.07 Günter-Hotz Koehler Exam Preparation (Extra Exercises)    

 

Exam: Wednesday, July 24, 14:00-16:00

Exam Inspection: Wednesday, July 31, 14:00-16:00

Second Exam: Monday, September 23, 14:00-16:00

Second Exam Inspection: Friday, September 27, 10:00-12:00



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