ICS 461: Artificial Intelligence

Description: Survey of artificial intelligence: natural language processing, vision and robotics, expert systems. Emphasis on fundamental concepts: search, planning, and problem solving, logic, knowledge representation.

Objectives: See learning outcomes.

Course Learning Outcomes

• To understand the historical, cultural and philosophical underpinnings of AI.
• To be familiar with the wide range of goals and approaches that define the field of AI.
• To understand the importance of representation and search in AI and in computer science in general, and to be able to describe key representation strategies and search algorithms.
• To understand and be able to describe in some detail some of the sub-fields of AI, including planning, machine learning, robotics, natural language processing and autonomous agents.
• To be familiar with the key algorithms associated with these sub-fields, and to be able to describe their strengths and weaknesses.
• Given a real-world problem, to be able to suggest AI approaches that might be appropriate for that problem.
• To be able to read and think critically about articles on current AI research and development.

Program Learning Outcomes

• a. Students can apply knowledge of computing and mathematics appropriate to the discipline
• b. Students can analyze a problem, and identify and define the computing requirements appropriate to its solution
• c. Students can design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs
• f. Students can communicate effectively with a range of audiences
• i. Students can use current techniques, skills, and tools necessary for computing practice

Prerequisites: 311 or consent.

Textbook(s): Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig

Grading: Assignments (6): 15% each, 90% total Online contributions: 10%

Assignments are strictly due at midnight on the due date. After that, assignments will lose 10% per day for each day late, up to five. After five days, the assignment will be graded out of 50%. All assignments must be completed satisfactorily to pass the course with a D grade or better.

Policies: Plagiarism and cheating are not tolerated in this course. If a student is caught cheating or plagiarizing, s/he will fail the course, and further disciplinary action may be taken.

Schedule

• Week 1: History and philosophy of AI
• Week 2: Intelligent agents
• Week 3 & 4: Search and heuristics
• Week 5 & 6: Logic and planning
• Week 7 & 8: Machine learning
• Week 9: Evolutionary computation
• Week 10 & 11: Natural language processing
• Week 12: Perception
• Week 13: Robotics
• Week 14: Review