AIML402 Advanced Artificial Intelligence

Overview

Modern AI representation systems and problem-solving techniques with a particular emphasis on machine learning.

About this paper

Restriction

COSC 343

Notes

Students with limited programming experience should also take AIML 401.

Contact

Computer Science Adviser

Teaching staff

Dr Lech Szymanski (Lecturer)

Paper Structure

In this paper we will focus on the hard problems to be solved in artificial intelligence (AI), 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 methods (including Bayesian methods)
• Machine learning algorithms (with a focus on neural networks and deep learning)

Teaching Arrangements

There are two lectures per week, weekly lab sessions, and three tutorials.

Textbooks

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

Graduate Attributes Emphasised

Interdisciplinary perspective, Scholarship, Communication, Ethics
View more information about Otago's graduate attributes.

Learning Outcomes

Students who successfully complete the paper will:

• Gain a practical understanding of a selection of core concepts in AI research: planning and search, probabilistic reasoning, machine learning, decision trees, neural networks, sequential decisions, and deep learning.
• Strengthen their understanding of each core concept through practical exercises and experience with implemented systems.

Timetable

Overview

Modern AI representation systems and problem-solving techniques with a particular emphasis on machine learning.

About this paper

Restriction

COSC 343

Notes

Students with limited programming experience should also take AIML 401

Contact

computing@otago.ac.nz

Teaching staff

Dr Lech Szymanski (Lecturer)

Paper Structure

In this paper we will focus on the hard problems to be solved in artificial intelligence (AI), 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 methods (including Bayesian methods)
• Machine learning algorithms (with a focus on neural networks and deep learning)

Teaching Arrangements

There are two lectures per week, weekly lab sessions, and three tutorials.

Textbooks

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

Graduate Attributes Emphasised

Interdisciplinary perspective, Scholarship, Communication, Ethics
View more information about Otago's graduate attributes.

Learning Outcomes

Students who successfully complete the paper will:

• Gain a practical understanding of a selection of core concepts in AI research: planning and search, probabilistic reasoning, machine learning, decision trees, neural networks, sequential decisions, and deep learning.
• Strengthen their understanding of each core concept through practical exercises and experience with implemented systems.

Timetable