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.
On the completion of the course the students should:
The textbook for this class is: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig. This book has a very good web site full of useful AI resources.
You hand in the (almost) weekly assignments. These 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.
You complete two programming assignments. This can be done as a group project (up to 3 people). Make sure you clearly indicate who is part of the group and that every group member clearly understands the solution.
The first programming assignment is to use search to find a good solution for a vacuum cleaning robot.
The second programming assignment is to program a connect-4 agent.
You can choose a topic for the final programming project (discuss topics and find a group in the forum). Like the programming assignments, this can be done as a group project (up to 3 people). You have to hand in a 1-2 page description of the project goal and some ideas on how to achieve it in week 8 (10% of the final grade) and demonstrate the final project in week 12 (20% of the final grade).
|Jan 12||2||Intelligent Agents|
|2||Jan 17||3||Search Problems|
|Jan 19||3||Blind Search|
|Jan 19||Lab||Lab 1 - Agents|
|3||Jan 24||3||Heuristic Search|
|Jan 26||3||Heuristic Search|
|Jan 26||Lab||Lab 1 - Agents|
|4||Jan 31||3||Heuristic Search|
|Feb 02||5||Adversarial Search|
|Feb 02||Lab||Lab 2 - Formulating Search Problems|
|5||Feb 07||Guest Lecture (Yngvi Björnsson)|
|6||Feb 14||5||Monte-Carlo Search, General Game Playing|
|Feb 16||7||Propositional Logic|
|Feb 16||Lab||Review of Programming Assignment 1, Programming Assignment 2|
|7||Feb 21||8||Propositional Logic|
|Feb 23||Lab||Lab 3 - Logic|
|Mar 01||Lab||Connect-4 Tournament|
|9||Mar 06||13,14||Uncertainty, Bayesian Networks|
|Mar 08||Lab||Lab 4 - Bayesian Networks|
|Mar 15||Lab||Lab 5 - Learning Decision Trees|
|Mar 22||Probabilistic Reasoning over Time|
|Mar 22||Lab||Lab 6 - Particle Filtering|
|Mar 29||Project Presentations|
|Mar 29||Lab||Project Presentations|
|Part of Course||Total Weight|
|2 Programming Assignments (2*10%)||20%|
|Final Written Exam||40%|