BDA323 - Advanced Artificial Intelligence

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA214 Machine learning

Co-requisite: N/A

Aims & Objectives

This is an elective 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 a related field by developing students’ specialised knowledge of advanced artificial intelligence technologies to address complex problems in data analytics and other similar disciplines. For further course information refer to: This unit is part of the AQF level 7 (BDA) course.

Students will learn to plan, design, implement and evaluate advanced artificial intelligence applications in advanced and diverse range of data analytic problems. They will gain knowledge of artificial intelligence strategies, model selection, training, testing and validation in data analytic applications. Students will gain hand on practical experience in deploying and testing artificial intelligence solutions to industrial applications that require critical analysis and technical currency.

This unit will cover the following topics:

  • Cognitive learning in artificial intelligence
  • Logic programming
  • Constraints reasoning
  • Bayesian models
  • Probabilistic graphical models 
  • Case based reasoning
  • Inductive learning
  • Distributed mining algorithms for association rules
  • Evolutionary computing
  • Multi-agent systems
  • Internet intelligence

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: 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Comprehend a range of advanced artificial intelligence topics in data analytics applications.;
b. Select and apply appropriate artificial intelligence solutions in complex problem demonstrating critical thinking skills;
c. Deploy and monitor artificial intelligence solutions in industry applications with good understanding of each approaches;
d. Evaluate artificial intelligence solution in consideration of system performance and business specifications;
e. Recommend appropriate artificial intelligence methodologies and solutions to a complex industry application. 

Weekly Topics

This unit will cover the following content:

Week Topics
1 History of artificial intelligence and cognitive learning
2 Logic programming
3 Constraints reasoning
4 Bayesian semantic models
5 Probabilistic graphical models (e.g. approximate inference)
6 Case based reasoning (e.g. instance-based learning)
7 Inductive learning 1: ID3 learning algorithm
8 Inductive learning 2: Bias shift-based decision tree algorithm
9 Distributed mining algorithms for association rules
10 Evolutionary computing
11 Multi-agent systems
12 Internet intelligence


Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B
Week 3
Week 8
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 ( 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


  • Zhongzhi Shi, Advanced Artificial Intelligence (Second Edition), World Scientific Publishing Company, August 2019 


  • Bengio, Yoshua, Ian Goodfellow, and Aaron Courville, Deep learning, MIT press, 2016.
  • Brownlee, Jason, Deep learning with python: Develop deep learning models on theano and tensorflow using keras, Machine Learning Mastery, 2016.
  • Michelucci, Umberto, Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks, Apress, 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


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.