Course Syllabus

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Meetings: MWF 1:50 - 2:50 PM, Olin 305 
Instructor: Peter Drake (Lab hours: 3-5 PM, Olin 305)
Text: none
 

Overview

This course examines the philosophical and practical issues involved in the design of thinking machines. We will explore the techniques used to get computers (and robots) to solve problems that once were (and in some cases still are) thought to be strictly in the domain of human intelligence.

CS 172 (Computer Science II) is a prerequisite for this course. You are expected to be proficient with the Java programming language, the Eclipse integrated development environment, and the JUnit testing system.

In terms of the ACM’s Computer Science Curriculum 2008, this course covers Intelligent Systems (Fundamental Issues, Basic Search Strategies, Advanced Search, Agents, Machine Learning, and Robotics).

Learning Objectives

Upon completing this course, you should be able to:
  • classify artificial intelligence tasks in terms of agents, environments, search, optimization, and learning.
  • use, implement, explain, and compare classical search algorithms, including depth-first, breadth-first, iterative-deepening, A*, and hill-climbing.
  • use, implement, explain, and compare adversarial search algorithms, including minimax (with alpha-beta pruning) and Monte Carlo tree search.
  • use, implement, explain, and compare machine learning techniques, including neural networks, and genetic algorithms.
  • build and program robots to perform simple tasks.
  • take and defend positions on philosophical issues relating to artificial intelligence, with reference to major ideas in the field.

Course Structure

This course is structured differently from others you may have taken. It is gamified in that it borrows some ideas from the gaming world to increase your motivation and ability to learn: achievements, fast and frequent feedback, and the ability to try until you succeed (with some necessary constraints).
 
Grading is based on achievements, attendance, quizzes, assignments, and exams, all listed under "Assignments" in the sidebar at left. (Achievements are extra credit within the "exams" section.) See my policies page for more details.

Groups

Assignments are done in groups of 4-5 in this course. I will assign groups on the first day.

 

Course Summary:

Course Summary
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