CSIS 440 Artificial Intelligence

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Course Description

An introduction to the basic concepts and techniques of artificial intelligence (AI), knowledge representation, reasoning and problem solving, AI search techniques, and moral and ethical considerations related to the use of AI-based systems. AI solutions will be developed in an appropriate AI language.

Instructor

J. Walker Orr, Ph.D.
Office hours: WMR 216 (see schedule)

Texts

recommended

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Ed. Pearson, 2020.
  ISBN: 978-0134610993 (US edition) or 978-1292401133 (Global edition)
• M. Mitchell, Artificial Intelligence: A Guide for Thinking Humans. Macmillan/Picador, 2019.
  ISBN: 978-1250758040

Resources

• Lectures
• In-class Activities

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Objectives

This course studies four main objectives of artificial intelligence (AI):

1. Modeling the environment by constructing computational representations of the real world
2. Perceiving and reasoning—obtaining and creating information (i.e., knowledge) to populate a computational representation
3. Taking action—use the knowledge of the environment and desired goals to plan and execute actions
4. Learning from past experience

Students will:

• Understand these basic objectives of AI and mechanisms for their implementation
• Use a high-level AI-oriented programming language to implement a simple representation and reasoning system
• Be familiar with the fundamental principles of propositional and predicate logic
• Be familiar with the fundamental principles of modern machine learning techniques
• Use a high-level programming language to implement various AI and machine learning solutions

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Course Organization

This course consists of lectures, group discussions, and hands-on programming exercises. Programming assignments will be carried out in the Prolog and Python programming languages. Some instruction in the use of these languages will be provided during lecture; however, I expect that you will consult additional resources to supplement your knowledge.

Collaboration

See the GFU CS/IS/Cyber policies for collaboration and discussion of collaboration and academic integrity. Most students would be surprised at how easy it is to detect collaboration in programming—please do not test us! Remember: you always have willing and legal collaborators in the faculty.

Almost all of life is filled with collaboration (i.e., people working together). Yet in our academic system, we artificially limit collaboration. These limits are designed to force you to learn fundamental principles and build specific skills. It is very artificial but intensional for your own benefit. The only way for you to learn is by doing the work.

To be clear, do not:

• Copy other students work
• Copy code from an online sources such as Stack Overflow
• Generate code with "AI" tools such as ChatGPT or GitHub Copilot

Spiritual Formation

Besides EYS, I am always available to discuss the Christian faith if you have any questions or doubts. Send me an email, come by my office hours, or talk to my after class, Christ is the reason I am at GFU, I always have time to talk about faith.

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Grading

The final course grade will be based on:

Grading Scale

• 50% Homework & programming assignments
• 40% Midterm & final exams
• 10% Participation & discussions

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Tentative Schedule

Week 1 · Introduction, History, Environment, & Agents Ch. 1 & 2

Week 2 · Problem Solving as Search Ch. 2

Week 3 · Heuristic Search Ch. 3

Week 4 · Local Search Ch. 4

2/13 · Mid-semester break—no classes -

Week 5 · Complex Environments Ch. 4

Week 6 · Adversarial Search Ch. 5

Week 7 · Constraint Satisfaction Ch. 6

Week 8 · Probability & Statistics Ch. 12

Week 9 · Intro. to ML Ch. 19

Week 10 · Linear Models Ch. 19.6

Week 11 · Spring Break – no class

Week 12 · Non-linear Models Ch. 19.7-8

Week 13 · Neural Networks & NLP Ch. 21

Week 14 · Large Language Models Ch. 23

Week 15 · Philosophy & Ethics Ch. 27

Week	Topic	Reading
Week 1	Introduction, History, Environment, & Agents	Ch. 1 & 2
Week 2	Problem Solving as Search	Ch. 2
Week 3	Heuristic Search	Ch. 3
Week 4	Local Search	Ch. 4
2/13	Mid-semester break—no classes	-
Week 5	Complex Environments	Ch. 4
Week 6	Adversarial Search	Ch. 5
Week 7	Constraint Satisfaction	Ch. 6
Week 8	Probability & Statistics	Ch. 12
Week 9	Intro. to ML	Ch. 19
Week 10	Linear Models	Ch. 19.6
Week 11	Spring Break – no class
Week 12	Non-linear Models	Ch. 19.7-8
Week 13	Neural Networks & NLP	Ch. 21
Week 14	Large Language Models	Ch. 23
Week 15	Philosophy & Ethics	Ch. 27

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1. GFU
2. CSIS
3. J. Walker Orr
4. CSIS 440