AI and Machine Learning
Undergraduate
Undergraduate
COMP 2019
Undergraduate
Yes
Note: This offering may or may not be scheduled in every study period. Please refer to the timetable for further details.
012520
4.5
No
UniSA STEM
To develop and deploy Artificial Intelligence and Machine Learning Systems.
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.
Stuart J. Russell., Peter Norvig 2009, Artificial Intelligence: A Modern Approach, 3rd, Prentice Hall
Subject Area & Catalogue Number | Course Name |
---|---|
Group 1
Students must have completed one of the following courses: |
|
COMP 1048 | UO Object Oriented Programming |
COMP 1046 | Object Oriented Programming |
COMP 2034 | Software Development |
Nil
Component | Duration | ||
---|---|---|---|
INTERNAL, MAWSON LAKES | |||
Lecture | 1 hour x 13 weeks | ||
Computer Practical | 2 hours x 13 weeks | ||
EXTERNAL, MAWSON LAKES, ONLINE | |||
Online | N/A x 13 weeks |
Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.
Continuous assessment, Group assignment, Test
EFTSL*: 0.125
Commonwealth Supported program (Band 2)
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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
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Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.
* 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.