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Overview

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

About this paper

Paper title Artificial Intelligence
Subject Computer Science
EFTS 0.15
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees ( NZD ) $1,318.20
International Tuition Fees Tuition Fees for international students are elsewhere on this website.
Prerequisite
COSC 201 or COSC 242
Recommended Preparation
COSC 202
Schedule C
Arts and Music, Science
Contact

adviser.computing@otago.ac.nz

Teaching staff

Lecturer: Dr Lech Szymanski

Paper Structure

In this paper we will look at some different definitions of intelligence and at the concept of intelligent agents, concentrating on the issue of how to get information about the world and how to make use of it. 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:

  • Search and optimisation algorithms (including genetic algorithms)
  • Probabilistic reasoning method (including Bayesian methods)
  • Machine learning algorithms (with a focus on neural networks)
Teaching Arrangements

2x 1h lectures per week

1x 2h lab sessions per week

Textbooks

Artificial Intelligence: A Modern Approach (Fourth Edition), by Stuart Russell and Peter Norvig, Pearson 2020.

Course outline

View the course outline for COSC 343

Graduate Attributes Emphasised
Interdisciplinary perspective, Scholarship, Communication, Ethics, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes

Students who successfully complete this paper will gain an understanding of a selection of core concepts in AI research: autonomous agents, planning and search, probabilistic reasoning, machine learning, decision trees, neural networks and sequential decisions.

  • 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.

Overview

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

About this paper

Paper title Artificial Intelligence
Subject Computer Science
EFTS 0.15
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees Tuition Fees for 2027 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.
Prerequisite
COSC 201 or COSC 242
Recommended Preparation
COSC 202
Schedule C
Arts and Music, Science
Contact

School of Computing Adviser

Teaching staff

Lecturer: Dr Lech Szymanski

Paper Structure

In this paper, we will look at some different definitions of intelligence and at the concept of intelligent agents, concentrating on the issue of how to get information about the world and how to make use of it. 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:

  • Search and optimisation algorithms (including genetic algorithms)
  • Probabilistic reasoning method (including Bayesian methods)
  • Machine learning algorithms (with a focus on neural networks)
Teaching Arrangements

Two 1-hour lectures per week

One 2-hour lab session per week

Textbooks

Artificial Intelligence: A Modern Approach (Fourth Edition), by Stuart Russell and Peter Norvig, Pearson 2020.

Course outline

View the course outline for COSC 343.

Graduate Attributes Emphasised
Interdisciplinary perspective, Scholarship, Communication, Ethics, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes

Students who successfully complete this paper will gain an understanding of a selection of core concepts in AI research: autonomous agents, planning and search, probabilistic reasoning, machine learning, decision trees, neural networks and sequential decisions.

  • 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.
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