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AIML402 Advanced Artificial Intelligence

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

Paper title Advanced Artificial Intelligence
Paper code AIML402
Subject Artificial Intelligence
EFTS 0.1667
Points 20 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,371.61
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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Pre or Corequisite
AIML 401
Restriction
COSC 343
Notes
Students with equivalent programming experience who do not meet the prerequisite should contact the department to discuss their eligibility.
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 reinforcement learning)
Teaching Arrangements

There are two lectures per week, plus weekly lab/tutorial sessions

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 and sequential decisions
  • Strengthen their understanding of each core concept through practical exercises and experience with implemented systems

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Timetable

Semester 2

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Computer Lab

Stream Days Times Weeks
Attend
A1 Wednesday 16:00-17:50 28-34, 36-41

Lecture

Stream Days Times Weeks
Attend
A1 Monday 09:00-09:50 28-34, 36-41
Wednesday 09:00-09:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend
A1 Tuesday 09:00-10:50 37-39