BDA214 - Machine learning

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA212 Statistics and Decision Models

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of data analytics in Information Technology. For further course information, refer to: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will be able to design, develop and evaluate machine learning models and algorithms. They will gain knowledge of supervised and unsupervised machine learning models and training algorithms, as well as practical skills in model construction, testing and validation in machine learning applications.

This unit will cover the following topics:

  • Feature selection and transformation
  • Machine learning model selection, training and testing
  • Supervised learning: decision forest, multi-layer perceptron, and support vector machine
  • Unsupervised learning: k-means clustering, self-organizing map
  • Ensemble learning (e.g. boosting and bagging)
  • Model regularization and overfitting
  • Machine learning applications in cybersecurity.

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Describe the needs of machine learning model in data analytic applications;
b. Appraise suitable machine learning models and algorithms to match requirements;
c. Design appropriate frameworks for machine learning model training, testing and validation;
d. Apply machine learning models and algorithms in data analytic applications;
e. Evaluate performance of machine learning models according to standards and specifications.
 

Weekly Topics

This unit will cover the following content:

Week Topics
1 Introduction to machine learning
2 Feature selection and feature transformation
3 Model selection, training, testing, and validations
4 Supervised learning 1: decision trees
5 Supervised learning 2: multi-layer perceptron
6 Supervised learning 3: support vector machine
7 Unsupervised learning 1: k-means clustering, mixture of Gaussians
8 Unsupervised learning 2: self-organising map
9 Semi-supervised learning – theory and concept
10 Ensemble methods (e.g. Decision Forest, Bagging and boosting)
11 Model robustness: overfitting and regularizations
12 Applications of machine learning in data analytics

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B		 Week 3
Week 8		 5%
10%		   a-c
Assignment 2 Week 11 25%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   50% a-e
TOTALS   50% 50%  

Task Type: Type A: unsupervised, Type B: supervised.

Class Participation and Contribution
This unit has class participation and student contribution as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Textbook:

  • Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition, O'Reilly Media, 2019.

References:

  • N. Dasgupta, Practical Big Data Analytics, Packt Publishing, 2018
  • Maheshwari, Data Analytics Made Accessible, Packt Publishing, 2018

Adopted Reference Style: IEEE

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.