This course has already ended.

Lectures and materials

Course Arrangements

Due to the still ongoing pandemic, this course will be held virtually.

Lecturer: Prof. Tapio Elomaa - tapio.elomaa@tuni.fi

Course Assistants:

Katri Leinonen - katri.leinonen@tuni.fi
Jenni Hukkanen - jenni.j.hukkanen@tuni.fi

Instead of traditional two-hour lectures, the course offers the students a number of introductions to various topics in the field of AI. These introductions are short video presentations with slides, around 30 minutes per topic. Introductions are published each week.

Q&A session timetable:

The participation in the online Q&A sessions is voluntary. In every session, two of the TAs are present in a Zoom call. The students may enter the call and ask questions about the course activities, and the TAs will do their best to assist the student.

The sessions are held each week from 2022-30-05 to 2022-07-17:

Tuesday, at 15-17.

Zoom link to session: https://tuni.zoom.us/j/69846958622?pwd=VFE2bmtmTEtZY3NaK21HRDJWaStaQT09
Passcode: 276151

If this time is not suitable for you, please contact the course staff!

Materials

Lecture Slides

The following list will be updated to include all the lecture slides as soon as they are published.

  1. Introduction
  2. Classical Search and Beyond
  3. Adversarial Search and Constraint Satisfaction Problems
  4. Probability and Probabilistic Inference
  5. Markov Decision Process and Reinforcement Learning
  6. Natural Language Processing

Book

The course is based on the book: Artificial Intelligence: A Modern Approach, by Stuart J. Russell and Peter Norvig.

Although the fourth edition has been published, the third edition is used due to its wider availability. The electronic book is available for students in the TUNI library. We have chosen to cover a set of five central AI topics in this course. Pattern recognition and machine learning algorithms are mostly covered in other courses.

The following textbook chapters will be included:

  1. CLASSICAL SEARCH AND BEYOND
    • Chapter 3: Solving Problems by Searching
    • Chapter 4: Beyond Classical Search
  2. ADVERSARIAL SEARCH AND CSPs
    • Chapter 5: Adversarial Search
    • Chapter 6: Constraint Satisfaction Problems
  3. PROBABILITY AND BAYESIAN NETWORKS
    • Chapter 13: Quantifying Uncertainty
    • Chapter 14: Probabilistic Reasoning
  4. MDPs AND REINFORCEMENT LEARNING
    • Chapter 17: Making Complex Decisions
    • Chapter 21: Reinforcement Learning
  5. NATURAL LANGUAGE PROCESSING
    • Chapter 22: Natural Language Processing
    • Chapter 23: Natural Language and Communication

Supporting Materials

Very useful lecture series on AI from Harvard University:

Another useful lecture series on AI from Berkeley University:

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