COMP3010 (v.1) Machine Learning
| Area: | School of Elec Eng, Comp and Math Sci (EECMS) |
|---|---|
| Credits: | 25.0 |
| Contact Hours: | 3.0 |
| TUITION PATTERNS: | The tuition pattern provides details of the types of classes and their duration. This is to be used as a guide only. Precise information is included in the unit outline. |
| Lecture: | 1 x 2 Hours Weekly |
| Tutorial: | 1 x 1 Hours Weekly |
| Prerequisite(s): |
COMP1000 (v.1)
Unix and C Programming
or any previous version
OR COMP1002 (v.1) Data Structures and Algorithms or any previous version OR 10163 (v.10) Unix and C Programming 120 or any previous version OR 1920 (v.8) Object Oriented Program Design 110 or any previous version |
| UNIT REFERENCES, TEXTS, OUTCOMES AND ASSESSMENT DETAILS: | The most up-to-date information about unit references, texts and outcomes, will be provided in the unit outline. |
| Syllabus: | Machine learning, its formulation, main problems and approaches. Familiarity with the formulation of machine learning from data. Familiarity with fundamental machine learning approaches including linear regression, linear classifiers, support vector machines, decision tree methods, Bayesian networks. Familiarity with the fundamentals of advanced machine learning techniques including feedforward neural networks, convolutional neural networks, residual neural networks and their training techniques. |
| Field of Education: | 020119 Artificial Intelligence |
| Result Type: | Grade/Mark |
Availability
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