Availabilities:
Unit Summary
Unit type
PG Coursework Unit
Credit points
12
AQF level
Level of learning
Introductory
Former School/College
Unit aim
Introduces students to machine learning. Students will take an algorithmic approach to machine learning in which real-world problems will be solved through machine learning techniques. Students will familiarise themselves with a wide range of algorithms and implement them for problem solving in Python/Octave.
Unit content
Topic 1: Intro to machine learning
Topic 2: Types of machine learning
Topic 3: Regression and prediction
Topic 4: Classification
Topic 5: Neural networks
Topic 6: Machine learning: best practices
Topic 7: Philosophy of machine learning
Learning outcomes
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
| On completion of this unit, students should be able to: | GA1 | GA2 | GA3 | GA4 | GA5 | GA6 | GA7 | |
|---|---|---|---|---|---|---|---|---|
| 1 | identify and evaluate the evolution and recent trends in computational intelligence and machine learning | |||||||
| 2 | critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts | |||||||
| 3 | develop, create and implement machine learning solutions to complicated problems | |||||||
| 4 | plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction | |||||||
On completion of this unit, students should be able to:
-
identify and evaluate the evolution and recent trends in computational intelligence and machine learning
- GA4:
- GA5:
-
critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts
- GA2:
- GA4:
- GA5:
-
develop, create and implement machine learning solutions to complicated problems
- GA2:
- GA4:
-
plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction
- GA2:
- GA4:
Prescribed texts
- Prescribed text information is not currently available.
- Geron, A, 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems , 2nd edn , O'Reilly.