Overview

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Academic contacts

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Offerings

DUBAI-ISC-TJD-INT-2022-2022
DUBAI-ISC-TMD-INT-2023-2023
DUBAI-ISC-TSD-INT-2022-2022
KAPLAN-SGP-TJA-INT-2022-2022
KAPLAN-SGP-TMA-INT-2023-2023
KAPLAN-SGP-TSA-INT-2022-2022
MURDOCH-S2-EXT-2021-ONGOING
MURDOCH-S2-INT-2021-ONGOING

To view more information about assessments, contacts and learning activities specific to one of these offerings, please select an offering from the drop-down menu above.

Other learning activities

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Learning activities

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Learning outcomes

1.

Describe and compare various supervised and non-supervised algorithms including dimensionality reduction, clustering and classification.

2.

Develop, using programming languages such as C/C++/Python, supervised and non-supervised algorithms.

3.

Train, test and evaluate the performance of algorithms and models.

4.

Application of best practices for data preparation.

5.

Describe the applications of Machine Learning (ML) and Artificial Intelligence (AI) in various application domains.

6.

Discuss and apply ethical principles to the use of ML and AI.

Assessments

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Additional information

Unit content:

The Unit will cover the following topics:

  1. Introduction and overview
  2. Unsupervised Learning – I: Dimensionality Reduction
  3. Unsupervised learning – II: Clustering
  4. Supervised Learning - I: Classification
  5. Supervised Learning – II: Neural Networks, Deep Neural Networks and Convolutional Neural Networks
  6. Non-gradient descent approaches Reinforcement learning
  7. Invited Industry/Research talk
Other notes:

Each student is expected to read the lecture notes and any recommended reading materials relevant to the topic for each week. Students will be able to access the unit information and learning materials through LMS. A list of relevant reference texts and resources will be provided. Students will also need to spend some time doing the lab exercises for that week. In addition, each student will need to complete two assignments on their own, and sit for the final examination. Assignments may require independent research to be carried out by students. Students with demonstrated capability may have the possibility to work with real data sets that may be subject to confidentiality agreements.