CSci 4511w, Spring 2018: Syllabus

Time/Room:	Tuesday and Thursday 4:00pm to 5:15pm in Amundson Hall B75
Instructor:	Dr. Maria Gini (gini at cs.umn.edu) office hours: Tuesday 3:00-4:00, Wednesday 3:00-4:00 and by appointment in Keller 4-225C, (612) 625-5582. Address: 4-192 EE/CSci Building, 200 Union St. SE, Mpls, MN 55455
TA:	Jamal Golmohammadi (golmo002@umn.edu), office hours: Wed 8:00-10:00 Arun Kumar (kumar250@umn.edu), office hours: Monday 9:00-10:00 Marie Manner (manne044@umn.edu), office hours: Monday 12:00-2:00 Akshay Mulkalwar (mulka002@umn.edu): office hour: Friday 2:00-3:00 Zechen Zhang (zhan5260@umn.edu), office hours: Friday 3:00-5:00 office hours in KHKH 2-209

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 available in many programming languages at https://github.com/aimacode. For the lisp software you can also go to http://aima.cs.berkeley.edu/lisp/doc/install.html and download it. You can download the python software from https://github.com/aimacode/aima-python. We will use the software for some homeworks.

All class material will be posted on the class page at http://www.cselabs.umn.edu/classes/Spring-2017/csci4511/. We will use moodle 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 of fundamental concepts of artificial intelligence (AI). Topics include: history of AI, agents, search (search space, uninformed and informed search, game playing, constraint satisfaction), planning, knowledge representation (logical encodings of domain knowledge, ontologies), and the programming language Lisp. 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

Readings: Approximatively 30 pages of reading/week from the texbook and occasionally other papers.
Participation: There will be an in-class exercise every week when there is no exam. Participation will count for 12% of the grade.
Assignments:
- 5 written homeworks (each 5% 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.
- 5 writing assignments (each 2-3% of the grade).
- one project (10% of the grade) on a topic of your own choice. It can be done in groups of two.
Exams (open book and notes):
- two in class midterm exams (10% each) and a final exam (20% of the grade).

Grades will be assigned on the following scale: 93% or above yields an A, 90% an A-, 87% a B+, 83% a B, 80% a B-, 75% a C+, 65% a C, 60% a C-, 55% a D+, 50% a D, below 50% an F.

Tentative Class Schedule (subject to changes)

Date	Ch	Topics	Assignments	AIMA Slides
Week 1 - Jan 16 and 18	1, 2	Intro. Intelligent Agents		Chapter 2
Week 2 - Jan 23 and 25	3	Problem Solving and Search	Writing 1 due (Thu Jan 25)	Chapter 3
Week 3 - Jan 30 and Feb 1	3,4	Search and Heuristic search	Homework 1 due (Thu Feb 1)	Chapter 4.1-2
Week 4 - Feb 6 and 8	4	Other search algorithms	Writing 2 due (Thu Feb 8)	Chapter 4
Week 5 - Feb 13 and 15	5	Game Playing	Homework 2 due (Thu Feb 15)	(Old) Chapter 6
Week 6 - Feb 20 and 22	6	Constraint Satisfaction.	Writing 3 due (Thu Feb 22)	(Old) Chapter 5
Week 7 - Feb 27 and Mar 1	6		First Midterm Exam (March 1)
Week 8 - Mar 6 and 8	7	Propositional Logic	Homework 3 postponed to Thu Mar 22	Chapter 7
Week 9 - Mar 20 and 22	8	First-Order Logic	Writing 4 due (Thu Mar 22)	Chapter 8
Week 10 - Mar 27 and 29	9	Inference in Logic	Homework 4 due (Thu Mar 29)	Chapter 9
Week 11 - Apr 3 and 5	10	Planning		(Old)Chapter 11
Week 12 - Apr 10 and 12	10	Planning	Second Midterm Exam (Thu Apr 12)	Graphplan
Week 13 - Apr 17 and 19	12	Knowledge Representation	Writing 5 due (Thu Apr 19)
Week 14 - Apr 24 and 26	20	Neural Networks and Deep Learning	Homework 5 due (Apr 26)	Neural Nets
Week 15 - May 1 and 3		Neural Networks and Deep Learning	Project report due (May 3)
			Final exam: Tuesday May 8, 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/).