Deakin University program

Machine Learning

Welcome to this Deakin University unit, SIT720: Machine Learning, where we will explore and apply machine learning techniques.

Explore machine learning techniques and apply them in real world problems.

Explore machine-learning techniques such as data representation, unsupervised learning (clustering and factor analysis) methods, supervised learning (linear and non-linear classification) methods, Bayesian learning that are used in the construction of neural networks. We also learn how to apply these techniques in real-world problem scenarios.

Courses

5 courses

Delivered by expert academics

Deakin University is Australia’s eighth largest university with more than 53,000 students from more than 100 countries studying online and on-campus. With world-class teaching and research, Deakin produces sought-after graduates poised to make a real difference in the world.

  • Deakin University

Who will you learn with?

Chandan Karmakar

Dr Karmakar is a Lecturer in Computer Science at the School of Information Technology. His research interests include Machine Learning, IoT in Healthcare and Biomedical Signal Processing.

Thanh Thi Nguyen

Dr Thanh Thi Nguyen is currently a Senior Lecturer in the School of Information Technology. He works with Deep Learning, Reinforcement Learning and is a leading researcher in Artificial Intelligence.

Learning outcomes

Trimester 2, 2018

  • Apply suitable clustering/dimensionality reduction techniques to perform unsupervised learning of data in a real-world scenario.
  • Work collaboratively and apply linear and logistic regression, and linear Support Vector Machines for designing accurate classifier.
  • Use the concept of maximum likelihood and Bayesian estimation to analyse, develop and compare various classification techniques.
  • Construct a multi-layer neural network using backpropagation training algorithm to demonstrate data representation, classification and evaluation skills.
  • Implement model selection and compute relevant evaluation measure for a given problem.

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