AI and Machine Learning

Course aim

To develop and deploy Artificial Intelligence and Machine Learning Systems.

Course content

Introduction to artificial intelligence; apply machine learning algorithms for classification and regression; search-based problem solving: heuristic search (A*, perhaps Planning); constraint satisfaction / optimisation;
evolutionary algorithms; adversarial search / stochastic methods MCTS (Alpha Zero); nature of ML methods;
supervised learning, data preparation, training; validation, overfitting; computer vision; tools and deployment;
applications in Natural Language Processing; embedding AI systems in the real world / ethics / problems.

Textbook(s)

Stuart J. Russell., Peter Norvig 2009, Artificial Intelligence: A Modern Approach, 3rd, Prentice Hall

Prerequisite(s)

Corequisite(s)

Nil

Teaching method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Programming exercise, Test/Quiz

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.