|Time/Room:||Monday and Wednesday 4:00pm to 5:15pm in Bruininks Hall 220.|
Dr. Maria Gini
(gini at umn.edu) |
office hours: Monday and Thursday 3:00-4:00 in Shepherd Lab 245 (612) 625-5582
Robert Giaquinto |
Stuart Russell and Peter Norvig
"Artificial Intelligence. A modern approach. 3rd Edition",
Prentice-Hall, 2010. ISBN: 9780136042594 (Chapters 1-12).
The software from the textbook is at https://github.com/aimacode. You can also download the lisp software from http://aima.cs.berkeley.edu/lisp/doc/install.html and the python software from https://github.com/aimacode/aima-python. We will use it for some homeworks.
All class material will be posted in canvas and on the class page at http://www.cselabs.umn.edu/classes/Spring-2020/csci4511W/. We will use canvas for homeworks, grades, and discussion.
Prerequisites: You are expected to have knowledge of basic computer science principles and programming; data structures (graphs and trees); and formal logic (propositional and predicate logic).
Course Description: The course provides a technical introduction to artificial intelligence (AI). Topics include: agents, search (search spaces and algortithms, game playing, constraint satisfaction), planning, knowledge representation, and an introduction to neural networks. The course is suitable to gain a solid technical background and as a preparation for more advanced work in AI.
Work Load and Grading Policy:
Tentative Class Schedule (subject to changes)
|Week 1 -   Jan 22||1-2||Intro, intelligent agents||Chapter 2|
|Week 2 -   Jan 27, 29||3||Problem solving and search||Writing 1 due (Wed Jan 29)||Chapter 3|
|Week 3 - Feb 3, 5||3-4||Search and heuristic search||Homework 1 due (Wed Feb 5)||Chapter 4.1-2|
|Week 4 - Feb 10, 12||4||Other search algorithms||Writing 2 due (Wed Feb 12)||Chapter 4|
|Week 5 - Feb 17, 19||5||Game playing||Homework 2 due (Wed Feb 19)||(Old) Chapter 6|
|Week 6 - Feb 24, 26||First midterm exam (Wed February 26)|
|Week 7 - Mar 2, 4||6||Constraint satisfaction.||Writing 3 due (Wed Mar 4)||(Old) Chapter 5|
|Week 8 - Mar 16, 18||7||Propositional logic||Homework 3 due (Wed Mar 18)||Chapter 7|
|Week 9 - Mar 23, 25||8-9||First-order logic and resolution||Writing 4 due (Wed Mar 25)||Chapter 8|
|Week 10 - Mar 30, Apr 1||9||Planning||Homework 4 due (Wed Apr 1)||Chapter 9|
|Week 11 - Apr 6, 8||10||Planning||Second midterm exam (Wed Apr 8)||(Old) Chapter 11|
|Week 12 - Apr 13, 15||Neural networks and deep learning||Writing 5 due (Wed Apr 15)|
|Week 13 - Apr 20, 22||Neural networks and deep learning||Homework 5 due (Wed Apr 22)||Neural Nets|
|Week 14 - Apr 27, 29||20||Knowledge representation||Project report due (Wed Apr 29)|
|3rd midterm: Monday May 4, 4:00pm-6:00pm|
Academic Integrity: All work submitted for the class must represent individual effort unless group work is explicitly allowed. You are free to discuss course material with classmates, TAs, and professor, but you should never misrepresent someone else's work as your own. It is your duty to protect your work from unauthorized access. Discussing answers and copying solutions from others in homeworks or exams is cheating and grounds for failing the course. Any student caught cheating will receive an F for the class and the University policies will be followed ( http://www.oscai.umn.edu/).