CSci 4511w, Spring 2019: Syllabus

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:

1. Readings: Approximatively 30 pages of reading/week from the texbook and occasionally other papers.
2. Participation: There will be an in-class exercise every week when there is no exam. Participation will count for 12% of the grade.
3. Assignments:
   • 4 written homeworks (each 6% of the grade). Homeworks will include problem solving and programming problems. Late homeworks must be submitted at the latest by Sunday evening of the week when they are due. They will lose 10% of the maximum total points.
   • 4 writing assignments (each 3% of the grade -- total 12%).
   • one project (12% of the grade) on a topic of your own choice. It can be done in groups of two.
4. Exams (open book and notes):
   • two in class midterm exams (10% each) and a final exam (20%).

Tentative Class Schedule (subject to changes)

Date	Ch	Topics	Assignments	AIMA Slides
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/).