31005 Machine Learning

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Subject handbook information prior to 2021 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): 31250 Introduction to Data Analytics AND 48024 Applications Programming

Recommended studies: knowledge of database technologies

Description

Machine learning is an exciting field studying of how intelligent agents can learn from and adapt to experience and how to realise such capacity on digital computers. It is applied in many fields of business, industry and science to discover new information and knowledge. At the heart of machine learning are the knowledge discovery algorithms. This subject builds on previous data analytics subjects to give an understanding of how both basic and more powerful algorithms work. It consists of both hands-on practice and fundamental theories. Students learn important techniques in the field by implementation and theoretical analysis. The subject also introduces practical applications of machine learning, especially in the field of artificial intelligence.

Subject learning objectives (SLOs)

1.	describe the scope, limitations and application of several advanced data machine learning methods;
2.	use or program a machine learning method;
3.	design an approach to machine learning problems in specialised domains;
4.	demonstrate an understanding of the issues underlying machine learning to successfully outline an approach to solving a machine learning problem.

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

• Socially Responsible: FEIT graduates identify, engage, interpret and analyse stakeholder needs and cultural perspectives, establish priorities and goals, and identify constraints, uncertainties and risks (social, ethical, cultural, legislative, environmental, economics etc.) to define the system requirements. (B.1)
• Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
• Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)

Teaching and learning strategies

The subject is delivered by online learning materials (organised videos of short lectures and algorithm implementation tutorials) and interactive workshops, as well as industry-based guest lectures. The subject features in-depth study of the theory and algorithm of data analytics, as well as detailed hands-on implementation tutorials of classical algorithms. Guest lectures also highlight UTS-specific and industry-based research that give students the opportunity to engage deeply with experts and ask questions that address advanced methods in data analytics. Students will engage with pre-reading material that will be used as basis for discussion and activities in class. Each week an in-class test is made available for students to check their knowledge and gauge their strengths and areas needing further practice. In-class tests with immediate feedback will help students to reflect on their learning. In this subject students have the opportunity to prepare a firm foundation for further study in data science and artificial intelligence by engaging deeply with the project.

Content (topics)

1. Machine learning and relationship to statistics and artificial intelligence
2. Theory of learning from data
3. Important learning models
4. Information theory
5. Evaluation method and decision making

Assessment

Assessment task 1: Quizzes

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 and C.1
Type:	Quiz/test
Groupwork:	Individual
Weight:	30%

Assessment task 2: Algorithm Implementation and Report

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and E.1
Type:	Project
Groupwork:	Individual
Weight:	50%
Length:	A report of about 1000 words

Assessment task 3: Video Presentation

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and E.1
Type:	Presentation
Groupwork:	Individual
Weight:	20%

Minimum requirements

In order to pass the subject, a student must achieve an overall mark of 50% or more.

Recommended texts

You might find the following texts useful.

1. Discovering Knowledge in Data, D. T. Larose and C. D. Larose, Wiley, 2014.
2. Learning from Data, Y. S. Abu-Mostafa, M. Magdon-Ismail and H-T. Lin, AMLbook.com, 2016.
3. Introduction to Data Mining, P.-N. Tan, M. Steinbach and V. Kumar, Addison-Wesley, 2005.
4. Machine Learning, Tom M Mitchell, McGraw-Hill, 1997.
5. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
6. Data Mining: Concepts and Techniques, J. Han and M. Kamber, Morgan Kaufmann, 2001.

References

The UTS Coursework Assessment Policy & Procedure Manual, at www.gsu.uts.edu.au/policies/assessment-coursework.html.

Other resources

online.uts.edu.au/
Copies of learning materials, assignments and general messages will be available at this web site.