CSE 327 Artificial Intelligence Theory and Practice (3)

Instructor

Jeffrey Heflin (Spring 2018)

Current Catalog Description

Introduction to the field of artificial intelligence: Problem solving, knowledge representation, reasoning, planning and machine learning. Use of AI systems or languages. Advanced topics such as natural language processing, vision, robotics, and uncertainty. CSE 261 is recommended. Prerequisite: CSE 017 or (CSE 001 and CSE 002).

Textbook

Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach", 3rd Edition, Prentice Hall, 2009, ISBN 978-0136042594

References

None

COURSE OUTCOMES

Students will have:

  1. Understanding of basic AI
  2. Knowledge of some examples of state of the art
  3. Understanding of the important issues and techniques in the subfields of AI

RELATIONSHIP BETWEEN COURSE OUTCOMES AND PROGRAM OUTCOMES

CSE 327 provides modest support to the following program outcomes:

A. An ability to apply knowledge of computing and mathematics appropriate to the discipline

B. An ability to analyze a problem and identify and define the computing requirements appropriate to its solution

I. An ability to use current techniques, skills, and tools necessary for computing practices

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices

K. An ability to apply design and development principles in the construction of software systems of varying complexity

Prerequisites by Topic

  • Comprehending a pseudo-code algorithm
  • Basic data structures (e.g., trees, graphs)

Major Topics Covered in the Course

  • Agents
  • Search
  • Game playing
  • Logic
  • Reasoning, including Prolog
  • Knowledge representation
  • Planning
  • Uncertainty
  • Machine learning

Assessment Plan for the Course:

The students are given seven homework assignments, a midterm and a final examination. Homework assignment questions typically do not involve programs, but instead demonstrate the steps of algorithms using diagrams, short essay question to analyze a topic, mathematical questions, etc. One homework assignment typically involves the use of Prolog as a logical reasoner. The midterm and final tend to have shorter versions of the types of questions that appear in the homeworks, as well as true/false questions. When I grade homeworks and exams, I look for common errors among students, and point these out in class.

How Data in the Course are Used to Assess Program Outcomes:(unless adequately covered already in the assessment discussion under Criterion 4)

Each semester I include the average grades and standard deviations of the items mentioned in the assessment plan above in my self-assessment of the course. I also provide a matrix that relates these assignments to program outcomes. This report is reviewed, in turn, by the Curriculum Committee.

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