COMP6670
Introduction to Machine Learning
- Credit
- 6 units
- Catalogue year
- 2027
- Recorded offerings
- Second Semester
Course information
Essential foundations for any machine learning application are a basic statistical analysis of the data to be processed, a solid understanding of the mathematical foundations underpinning machine learning as well as the basic classes of learning/adaptation concepts. Those foundations are bundled in this single, introductory course to machine learning in preparation for deeper explorations into the topic, but also as a standalone unit.
Requisite and Incompatibility
To enrol in this course you must: be enrolled in Master of Computing (Advanced) OR have completed or be currently enrolled in (COMP6710 or COMP7710 or COMP6730) OR have completed COMP1110 or COMP1140. Incompatible with COMP3670.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Develop an appreciation for what is involved in learning via data-driven approaches, like data collection, data safety and privacy, and ethics in machine learning.
Describe a variety of machine learning tasks, like regression and classification, clustering, dimensionality reduction.
Understand how to formalise practical problems and evaluate model performance of machine learning tasks.
Interpret mathematical equations from linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
Design mathematical and statistical models using linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
Analyse advanced machine learning technologies including reinforcement learning, neural networks, and large models
Saved ANU Programs & Courses information, 2027. Future offerings are indicative. Checks use the saved requirements and your fictional academic record.
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