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T-622-ARTI, Introduction to Artificial Intelligence, Spring 2015


Artificial Intelligence (AI) is devoted to the computational study of intelligent behaviour, including areas such as problem solving, knowledge representation, reasoning, planning and scheduling, machine learning, perception and communication. This course gives an overview of the aforementioned AI subfields from a computer science perspective and introduces fundamental solution techniques for addressing them. An important part of the course is an independent final project where the students develop AI software in an area of their choice.


For questions and discussions about the lectures, homework, projects and AI in general go to the Piazza page of the course.


On the completion of the course the students should:

  • have a good overview of the field of artificial intelligence (AI) and a thorough understanding of the fundamental solution methods used to attack a wide variety of AI-related problems.
  • have gained experience building a small special-purpose AI system.


The textbook for this class is: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig. This book has a good web site full of useful AI resources.

Coursework Overview

Homework Assignments/Labs (20%)

You hand in the (almost) weekly homework assignments and finish the labs. The assignments will mainly consist of small exercises in which you have to apply what you should have learned in the lecture. The questions should give you an indication of the questions that may be asked in the final exam. The labs are more practical applications of the material, often in the form of small programming tasks.

Programming assignments (2 x 10%)

You complete two programming assignments. This can be done as a group project (up to 4 people). Make sure you clearly indicate who is part of the group and that every group member clearly understands the solution.

Final Project (20%)

You can choose a topic for the final programming project (discuss topics and find a group on the Piazza page. Like the programming assignments, this can be done as a group project (up to 4 people). You have to hand in a 1-2 page description of the project goal and some ideas on how to achieve it approx. in week 8 (5% of the final grade) and a report in the last week (15% of the final grade).

Exam (40%)

There will be a final exam (3h) with questions similar to the ones in the assignments.


Part of CourseTotal Weight
Assignments, Labs 20%
2 Programming Assignments (2*10%) 20%
Final Project 20%
Final Written Exam 40%
Total 100%

Course Schedule (subject to change)

1Jan 13 1Introduction, History
Jan 14 LabLab 1 - Agents
Jan 15 2Intelligent Agents, Search Problems
2Jan 20 3Search Problems, Blind Search
Jan 21 LabLab 2 - Hashing States
Jan 22 3Blind Search, Heuristic Search
3Jan 27 3Heuristic Search
Jan 28 LabProgramming Assignment 1 - Search
Jan 29 5Adversarial Search (Minimax, Alpha-Beta)
4Feb 03 5,6Adversarial Search (Algorithms), CSPs
Feb 04 LabProgramming Assignment 1 - Search
Feb 05 6CSPs
5Feb 10 7Propositional Logic
Feb 11 LabLab 3 - CSPs
Feb 12 7Propositional Logic, Logical Agents
6Feb 17 7,8,9Logical Agents, First Order Logic
Feb 18 LabProgramming Assignment 2 - Connect 4
Feb 19 8,9,10First Order Logic, Planning
7Feb 24 10Planning
Feb 25 LabLab 4 - Propositional Logic
Feb 26 13, 14Uncertainty, Bayesian Networks
8Mar 03 13, 14Bayesian Networks
Mar 04 LabLab 5 - Bayesian Networks
Mar 05 18-21Machine Learning
9Mar 10 18.3Learning Decision Trees
Mar 11 LabProgramming Assignment 2 - Competition
Mar 12 25Robotics
10Mar 17 15Probabilistic Reasoning over Time
Mar 18 LabLab 6 - Learning Decision Trees
Mar 19 15Probabilistic Reasoning over Time
11Mar 24 ???
Mar 25 LabLab 7 - Particle Filtering
Mar 26 ???
13Map 31 Wrap-Up
Easter Break
Apr 08 LabProject Presentations
Apr 09 Project Presentations
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