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T-622-ARTI, Introduction to Artificial Intelligence, Spring 2016

Description

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.

Help

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

Goals

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.

Book

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, ideally 2-3). Make sure you clearly indicate who is part of the group and that every group member clearly understands the solution.

Project (20%)

You can choose a topic for the 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 7 (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.

Grading

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

Course Schedule (subject to change)

WeekDateChaptersTopic
1Jan 12 1Introduction, History
Jan 13 LabLab 1 - Agents
Jan 14 2Intelligent Agents, Search Problems
2Jan 19 3Search Problems, Blind Search
Jan 20 LabLab 2 - Hashing States
Jan 21 3Blind Search, Heuristic Search
3Jan 26 3Heuristic Search
Jan 27 LabProgramming Assignment 1 - Search (description on MySchool)
Jan 28 5Adversarial Search (Minimax, Alpha-Beta)
4Feb 02 5,6Adversarial Search (Algorithms), CSPs
Feb 03 LabProgramming Assignment 1 - Search (description on MySchool)
Feb 04 6CSPs
5Feb 09 7Propositional Logic
Feb 10 LabLab 3 - CSPs
Feb 11 7Propositional Logic, Logical Agents
6Feb 16 7,8,9Logical Agents, First Order Logic
Feb 17 LabProgramming Assignment 2 - Connect 4
Feb 18 8,9,10First Order Logic, Planning
7Feb 23 10Planning
Feb 24 LabLab 4 - Propositional Logic
Feb 25 13, 14Uncertainty, Bayesian Networks
8Mar 01 13, 14Bayesian Networks
Mar 02 LabLab 5 - Bayesian Networks
Mar 03 18-21Machine Learning
9Mar 08 18.3Learning Decision Trees
Mar 09 LabProgramming Assignment 2 - Competition
Mar 10 25Robotics
10Mar 15 15Probabilistic Reasoning over Time
Mar 16 LabLab 6 - Learning Decision Trees
Mar 17 15Probabilistic Reasoning over Time
11Mar 22 ???
Easter Break
12Mar 30 LabLab 7 - Particle Filtering
Mar 31 ???
13Apr 05 Wrap-Up
Apr 06 LabProject Presentations
Apr 07 Project Presentations
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