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Overview

This subject introduces students to the fundamentals of machine learning (history, algorithm types, uses) and then covers an efficient implementation of machine learning algorithms on real-world data of moderate complexity using an open source ecosystem and MapReduce and R environments. Students explore the practical aspects of this subject using the … For more content click the Read more button below.

Portfolio

Office of the Provost

School

Computing, Engineering and Mathematical Sciences

Subject coordinator

Rabei Alhadad

Subject type

Undergraduate

Year level

Year Level 2 - UG

AQF level

Level 6 - Advanced Diploma

Available as elective

No

Available to study abroad / exchange students

No

Capstone subject

No

Academic progress review - Schedule A subject

No

Subject instances

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Learning resources

Recommended - Book - Amazon Web Services online training

Prescribed - Book - Machine Learning: Hands-On for Developers and Technical Professionals

Career ready

Work based learning (placement):No

Graduate capabilities

COMMUNICATION - Communicating and Influencing
INQUIRY AND ANALYSIS - Creativity and Innovation
INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving
INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry
PERSONAL AND PROFESSIONAL - Adaptability and Self-Management

Subject intended learning outcomes

On successful completion you will be able to:
1.
Appraise machine learning as it can be applied to real-world problems in the cloud environment
2.
Analyse the strengths and weaknesses of a wide variety of machine learning algorithms
3.
Apply machine learning algorithms to solve real-world problems of moderate complexity
4.
Compare the various tools that are available for data collection and processing
5.
Integrate machine learning libraries, and mathematical and statistical tools with modern technologies like Hadoop distributed file system and MapReduce programming model
6.
Employ R programming language to perform efficient implementation of machine learning algorithms on real data

Requisite rules

Prerequisites: Students must be admitted in one of the following courses: SBACTO, SBAIO, LBAB, LBABK, LBABM