Artificial Intelligence

University of Otago

Course Description

  • Course Name

    Artificial Intelligence

  • Host University

    University of Otago

  • Location

    Dunedin, New Zealand

  • Area of Study

    Computer Science

  • Language Level

    Taught In English

  • Prerequisites

    COSC 242

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • Credit Points

  • Recommended U.S. Semester Credits
    3 - 4
  • Recommended U.S. Quarter Units
    4 - 6
  • Overview

    An introduction to modern AI representation systems and problem-solving techniques.

    Teaching Arrangements
    There are two lectures per week, plus weekly lab or tutorial sessions.

    Course Structure
    In the first part of the paper we will look at some different definitions of intelligence and at the concept of intelligent agents. As a way of focusing on the hard problems to be solved in AI, we will do some practical work with LEGO robots, concentrating on the issue of how to get information about the world and how to make use of it. In the second part of the paper, we will consider techniques for machine learning and probabilistic reasoning. Almost every human ability results from learning from experience: we will look at how these learning processes can be modelled computationally. Topics to be considered include:
    - Decision trees
    - Genetic algorithms
    - Neural networks
    - Bayesian networks and Bayesian reasoning
    - Probabilistic reasoning over time
    - Dynamic Bayesian networks
    - Hidden Markov models
    - Statistical learning methods

    - Two assignments 28%
    - Participation in tutorials and tutorial exercises 12%
    - Final exam 60%

    Learning Outcomes
    - Students will gain an understanding of some of the core concepts in symbolic AI research: autonomous agents, planning and search, logical knowledge representation formalisms, grammars for representing knowledge of natural language
    - Students will gain an understanding of some of the core concepts in sub-symbolic AI research: sensory perception, motor control, machine learning, decision trees, neural networks
    - In each case, this understanding will be strengthened through practical exercises and experience with implemented systems
    -The paper will also give students an awareness of the increasing influence of these technologies in daily life

    Artificial Intelligence A Modern Approach (Third Edition) , by Stuart Russell and Peter Norvig, Prentice Hall 2010.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Some courses may require additional fees.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.