CSci 4511w, Spring 2020: Syllabus

Time/Room: Monday and Wednesday 4:00pm to 5:15pm in Bruininks Hall 220.
Instructor:
 Dr. Maria Gini (gini at umn.edu)
office hours: Monday and Thursday 3:00-4:00 in Shepherd Lab 245 (612) 625-5582
TAs: Robert Giaquinto : office hours Tuesday 12:00-2:00 in Keller 2-209
Nabil Khan : office hours Thursday 10:00-12:00 in Shepherd 159
Ritchie Paul : office hours Wednesday 10:30-12:30 in Keller 2-209
James Zhang : office hours Monday 10:00-12:00 in Keller 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 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:

  1. Readings: About 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. Exams (open book and notes):
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 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/).