|Time/Room:||Tuesday and Thursday 4:00pm to 5:15pm in Amundson Hall B75|
Dr. Maria Gini
(gini at cs.umn.edu) |
office hours: Wed 10:30 to 11:30, Thursday 11:00 to 12:00 in Shepherd Lab 245 (612) 625-5582.
Address: 4-192 Keller, 200 Union St. SE, Mpls, MN 55455
Carter Blum |
Textbook: 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-2019/csci4511/. We will use canvas to submit homework, for grades, and for the class forum.
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: This course provides a technical introduction to fundamental concepts of artificial intelligence (AI). Topics include: history of AI, agents, search (search spaces, uninformed/informed search, game playing, constraint satisfaction), planning, and knowledge representation (ontologies). 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, 24||1-2||Intro, intelligent agents||Chapter 2|
|Week 2 -   Jan 29, 31||3||Problem solving and search||Chapter 3|
|Week 3 - Feb 5, 7||3-4||Search and heuristic search||Homework 1 due (Thu Feb 7)||Chapter 4.1-2|
|Week 4 - Feb 12, 14||4||Other search algorithms||Writing 1 due (Thu Feb 14)||Chapter 4|
|Week 5 - Feb 19, 21||5||Game playing||Homework 2 due (Thu Feb 21)||(Old) Chapter 6|
|Week 6 - Feb 26, 28||6||Constraint satisfaction.||Writing 2 due (Thu Feb 28)||(Old) Chapter 5|
|Week 7 - Mar 5, 7||First midterm exam (March 7)|
|Week 8 - Mar 12, 14||7||Propositional logic||Homework 3 due (Thu Mar 14)||Chapter 7|
|Week 9 - Mar 26, 28||8-9||First-order logic and resolution||Writing 3 due (Thu Mar 28)||Chapter 8|
|Week 10 - Apr 2, 4||9||Planning||Homework 4 due (Thu Apr 4)||Chapter 9|
|Week 11 - Apr 9, 11||10||Planning||Second midterm exam (moved to Apr 16)||(Old) Chapter 11|
|Week 12 - Apr 16, 18||Neural networks and deep learning||Writing 4 due (Thu Apr 18)|
|Week 13 - Apr 23, 25||Neural networks and deep learning||Extra homework 5 due (Thu Apr 25)||Neural Nets|
|Week 14 - Apr 30, May 2||20||Knowledge representation||Project report due (May 2)|
|Final exam: Thursday May 9, 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 responsibility to protect your work from unauthorized access. Discussing answers to problems and copying solutions from others in homeworks or exams is considered cheating and grounds for failing the course. Any student caught cheating will receive an F as a class grade and the University policies will be followed (see http://www.oscai.umn.edu/).Copyright: © 2019 by the Regents of the University of Minnesota