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Modern AI representation systems and problem-solving techniques with a particular emphasis on machine learning.
|Paper title||Advanced Artificial Intelligence|
|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.|
- Pre or Corequisite
- AIML 401
- COSC 343
- Students with equivalent programming experience who do not meet the prerequisite should contact the department to discuss their eligibility.
- Teaching staff
- 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
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