CSCI 4511w -- New Course

Fri Jan 23 13:38:28 2009

Approvals Received:
Department
on 01-23-09
by Mary Freppert
(freppert@umn.edu)
Approvals Pending: College/Dean  > LE > Catalog > PeopleSoft Manual Entry
Effective Status: Active
Effective Term: 1103 - Spring 2010
Course: CSCI 4511W
Institution: UMNTC - Twin Cities
Career: UGRD
College: TIOT - Institute of Technology
Department: 11108 - Computer Science & Eng
General
Course Title Short: Intro: Artificial Intelligence
Course Title Long: Introduction to Artificial Intelligence
Max-Min Credits
for Course:
4.0 to 4.0 credit(s)
Catalog
Description:
Introduction to AI. Problem solving, search, inference techniques. Knowledge representation.  Planning.  Introduction to machine learning. Robotics. The Lisp programming language.
CCE Catalog
Description:
<no text provided>
Grading Basis: Stdnt Opt
Topics Course: No
Honors Course: No
Delivery Mode(s): Classroom
Instructor
Contact Hours:
4.0 hours per week
Years most
frequently offered:
Every academic year
Term(s) most
frequently offered:
Spring
Component 1: LEC (with final exam)
Auto-Enroll
Course:
No
Graded
Component:
LEC
Academic
Progress Units:
Not allowed to bypass limits.
4.0 credit(s)
Financial Aid
Progress Units:
Not allowed to bypass limits.
4.0 credit(s)
Repetition of
Course:
Repetition not allowed.
Course
Prerequisites
for Catalog:
CSci 2011 or #;cannot be taken for grad CSci cr.
Course
Equivalency:
CSci 4511W/5511
Consent
Requirement:
No required consent
Enforced
Prerequisites:
(course-based or
non-course-based)
No prerequisites
Editor Comments: <no text provided>
Proposal Changes: <no text provided>
History Information: <no text provided>
Faculty
Sponsor Name:
Maria Gini
Faculty
Sponsor E-mail Address:
gini@cs.umn.edu
Liberal Education
Requirement
this course fulfills:
None
Other requirement
this course fulfills:
None
Criteria for
Core Courses:
Describe how the course meets the specific bullet points for the proposed core requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or method.

Core courses must meet the following requirements:

  • They explicitly help students understand what liberal education is, how the content and the substance of this course enhance a liberal education, and what this means for them as students and as citizens.
  • They employ teaching and learning strategies that engage students with doing the work of the field, not just reading about it.
  • They include small group experiences (such as discussion sections or labs) and use writing as appropriate to the discipline to help students learn and reflect on their learning.
  • They do not (except in rare and clearly justified cases) have prerequisites beyond the University's entrance requirements.
  • They are offered on a regular schedule.
  • They are taught by regular faculty or under exceptional circumstances by instructors on continuing appointments. Departments proposing instructors other than regular faculty must provide documentation of how such instructors will be trained and supervised to ensure consistency and continuity in courses.

<no text provided>
Criteria for
Theme Courses:
Describe how the course meets the specific bullet points for the proposed theme requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or methods.

Theme courses have the common goal of cultivating in students a number of habits of mind:
  • thinking ethically about important challenges facing our society and world;
  • reflecting on the shared sense of responsibility required to build and maintain community;
  • connecting knowledge and practice;
  • fostering a stronger sense of our roles as historical agents.


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Writing Intensive
Propose this course
as Writing Intensive
curriculum:
Yes
Question 1: What types of writing (e.g., reading essay, formal lab reports, journaling) are likely to be assigned? Include the page total for each writing assignment. Indicate which assignment(s) students will be required to revise and resubmit after feedback by the instructor or the graduate TA.

Writing computer programs with appropriate comments and documentation is an essential form of writing for computer scientists. Students will be given short programming assignments during the semester and asked to provide explanations for their programs. In addition, students will be asked to do other various other types of writing, from the following list:

1.        write a report on their project.  The project will be required for everyone. The project will be proposed by the students on a topic of their own choice related to any aspect of the material covered in the course, including search algorithms, computer games, robotics, etc. The report will be due in different parts: a 1-2 pages proposal, a 4-5 pages preliminary report, and a 10 pages final report.  Students will be given a chance to resubmit their work for both the proposal and the preliminary report, with suggestions to improve their writing.
2.        write a critique to a paper published in the field. This will be done individually and limited to one page. Students will have to write 2 or 3 critiques and will be allowed to resubmit them.
3.        write some short essays as part of the homeworks.  The amount of writing and length of the essays will likely change every semester, most essays will be short, limited to one page, and students will have to write 2 to 3 such essays.
4.        students will also be required to contribute to the class discussion in the on-line class forum.  
Question 2: How does assigning a significant amount of writing serve the purpose of this course?

Good skills in writing and expressing ideas are essential for Artificial Intelligence. Artificial Intelligence requires, more than other parts of Computer Science, the ability to think logically and clearly, the ability to understand issues, articulate ideas, motivate opinions, and describe choices.
Question 3: What types of instruction will students receive on the writing aspect of the assignments?

Guidelines and material on technical writing will be posted on the class webpage and covered in class.  The material will span from short guidelines on technical writing, to material on formatting technical papers using Latex, to pointers to comprehensive writing material and grammar rules, to specific guidelines for the writing assignments, and examples of the types of writing expected in the course.
The students' writings will be reviewed for technical content, clarity, and quality.   Most of the writings will be reviewed and graded by the instructor, with help from the TAs for the homework essays and with help from the students themselves for the critique papers. The critique papers will be initially reviewed by other students in the class, using informal discussion groups, and will then graded by the instructor. Students will be allowed to resubmit them. The project report will be submitted in multiple rounds, with feedback given to the students after the proposal submission and after the preliminary report. Specific guidelines and detailed requirements for all the writing assignments will be discussed in class.
Question 4: How will the students' grades depend on their writing performance? What percentage of the overall grade will be dependent on the quality and level of the students' writing compared with the course content?

The students' grades will depend on their writing performance in the sense that no student will be allowed to pass the class without satisfactory performance in writing. A significant part of the class grade (20%) is based on the project, and 40% of the grade for the project will depend on the content, format, and clarity of the writing of the project report.  The critique papers and essay questions will count for 10-15% of the grade. Various in class activities and writing on the class forum will count for 10% of the grade.
Question 5: If graduate students or peer tutors will be assisting in this course, what role will they play in regard to teaching writing?

The TAs will be closely supervised by the instructor.  They will receive instruction from the professor in how to evaluate the content, structure, and clarity of the project report.
Question 6: How will the assistants be trained and supervised?

The TAs will be supervised by the instructor.  The Department will provide training as needed.
Question 7: Write up a sample assignment handout here for a paper that students will revise and resubmit after receiving feedback on the initial draft.

This assignment is to familiarize you with some of the ideas (and hopefully to dispel the myths) about Artificial Intelligence. Do the following:

1.        Read a paper of your choice from The Singularity Issue of IEEE Spectrum. This is a recent issue that includes a collection of papers written by a variety of scientists and philosophers.  The papers are short and written for a general technically educated public.
2.        Write 1 page summarizing briefly the major points of the paper you have read and with your own comments/critique to the paper. Why did you select the paper?  Did the paper present compelling arguments? scientific evidence?
3.        Bring your writing to class.  We will discuss the ideas form the papers in small groups and criticize each other writing.  You will be allowed to rewrite your critique before submitting it for grading.
Course Syllabus
Course Syllabus: For new courses and courses in which changes in content and/or description and/or credits are proposed, please provide a syllabus that includes the following information: course goals and description; format;structure of the course (proposed number of instructor contact hours per week, student workload effort per week, etc.); topics to be covered; scope and nature of assigned readings (text, authors, frequency, amount per week); required course assignments; nature of any student projects; and how students will be evaluated. The University "Syllabi Policy" can be found here

The University policy on credits is found under Section 4A of "Standards for Semester Conversion" found here. Course syllabus information will be retained in this system until new syllabus information is entered with the next major course modification. This course syllabus information may not correspond to the course as offered in a particular semester.

(Please limit text to about 12 pages. Text copied and pasted from other sources will not retain formatting and special characters might not copy properly.)


CSci 4511W: Sample Syllabus
Class Information
Time/Room:         TBA
Instructor:        Dr. Maria Gini (gini at cs.umn.edu)
office hours: Monday TBA or by appointment in EE/CS 5-213, (612) 625-5582.
Address:4-192 EE/CSci Bldg, 200 Union St. SE, Mpls,MN 55455
TAs:         TBA

Textbook
Stuart Russell and Peter Norvig "Artificial Intelligence. A modern approach. 2nd Edition", Prentice-Hall, 2003. (Chapters 1-11, 18, 20, 25).
You should go to http://aima.cs.berkeley.edu/lisp/doc/install.html to download the Lisp software from the texbook. We will use it for some homeworks.
You'll need reference material on Lisp. Here are some choices:
•        Lamkins, Successful Lisp: How to Understand and Use Common Lisp, bookfix.com, 2004.
•        Graham, ANSI Common Lisp, Prentice Hall, 1996
The Lisp Code, the text of the first two chapters, and many articles on Lisp are available from http://www.paulgraham.com/
All class material will be posted at http://www.itlabs.umn.edu/classes/Spring-2000/csci4511w/.

Prerequisites
Students are expected to have the following background:
•        Knowledge of basic computer science principles.
•        Knowledge of data structures (graphs and trees).
•        Knowledge of formal logic (propositional and predicate logic).

Course Description
This course provides a technical introduction of fundamental concepts of artificial intelligence (AI) and their applications to real world problems. Topics include: history of AI, agents, search (search space, uninformed and informed search, constraint satisfaction, game playing), knowledge representation (logical encodings of domain knowledge, logical reasoning systems), planning, an introduction to machine learning, and the language Lisp. The course is suitable for students who want to explore the field of Artificial Intelligence and build the foundations for  more advanced work in AI.

Course Work and Learning Objectives
Students are expected to read approximately 30 pages/week from the textbook. You are expected to spend 12 hours/week on average for the course, including class time, lab time, reading the textbook, studying, and doing the homework.  
The course is writing intensive and will include some writing instruction and various forms of writing:
•        A report on your class project will be due in 3 parts: a proposal (1-2 pages), a preliminary report (4-5 pages) and a final report (10 pages).  You will be allowed to resubmit both the proposal and the preliminary report.
•        We will read 2-3 papers in addition to the material in the textbook.  A written summary and critique (1 page) on each paper will be due.  Each critique will be discussed in class in small groups.  You will be allowed to rewrite it and resubmit it.
•        Each homework will include one short essay question (1-2 pages).
•        Contributions to the discussion on the class forum are expected form everyone in the class.
Course Requirements
•        five written homeworks (40% of the grade). Homeworks will include problem solving, short essays, and Lisp programming problems;
•        one project (20% of the grade). The project is on a topic of your own choice and can be done in groups of two.
•        participation to class discussion and other in class activities (10% of the grade).
•        two in class midterm exams (each 10% of the grade),
•        one final exam (10% of the grade).

Academic Integrity
All work submitted for this class must represent your own individual effort unless group work is explicitly allowed. You are free to discuss course material and approaches to problems with classmates, the TAs, and the professor (and you are encouraged to do so), but you should never misrepresent someone else's work as your own. It is also your responsibility to protect your work from unauthorized access. Collaboration on homework or exams is cheating and grounds for failing the course. Any student caught cheating will receive an F as a class grade and the University policies for cheating will be followed. In addition, any graduate student caught cheating will be subject to the Department policy on cheating.

Policy on Exams and Grading
Grades will be assigned on the following scale: 93% and up will earn you an A 90% to 93% an A-, 87% to 90% a B+, 83% to 87% a B, 80% to 83% a B-, 75% to 80% a C+, 65% to 75% a C, 60% to 65% a C-, 55% to 60% a D+, 50% to 55% a D, below 50% an F.
Exams are open books and notes. Late homeworks will lose 10% of the maximum total points for every weekday late. Late homeworks will be accepted up to a week after they are due. Keys will be distributed in class a week after the homework is due.

Tentative Class Schedule (subject to changes)
        Ch        Topics        Assignments due            AIMA Slides
Week 1         1, 2         Intro to AI. Intelligent Agents                 Chapter 2
Week 2         3         Problem Solving and Search         Homework 1         Chapter 3
Week 3         3,4         Search Algorithms                Chapter 4.1-2
Week 4          4         Heuristic Search         Homework 2         Chapter 4

Week 5          5         Constraint Satisfaction                 Chapter 5
Week 6          6         Game Playing         First Midterm Exam         Chapter 6
Week 7         6         Game Playing, Game Theory               
Week 8         7         Propositional Logic        Homework 3
Project Proposal         Chapter 7
Week 9         8, 9         First-Order Logic                 Chapter 8 and 9
Week 10          18        Machine Learning, Decision trees         Homework 4         Chapter 18
Week 11          20        Reinforcement Learning                Chapter 20
Week 12         10         Knowledge Representation         Second Midterm Exam         
Week 13         25         Introduction to Robotics                 Chapter 25
Week 14         11        Planning         Homework 5         Chapter 11
Week 15                   Wrap up        Project Report       
                        Final Exam
       
Copyright: 2008 by the Regents of the University of Minnesota
Department of Computer Science and Engineering. All rights reserved.
Comments to: Maria Gini