Portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 2
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Postgraduate Course, University of New South Wales, School of Computer Science and Engineering, 2020
Postgraduate Course, University of New South Wales, School of Computer Science and Engineering, 2021
Tutor of an introductory course on Artificial Intelligence covering fundamental topics, such as autonomous agents, problem solving, search, logic, knowledge representation, reasoning under uncertainty, natural language processing, machine learning and neural networks.
Postgraduate course, University of New South Wales, School of Computer Science and Engineering, 2022
Tutor of a course that provides an introduction to core ideas and techniques in machine learning (ML), covering theoretical foundations, algorithms, and practical methodology. Algorithms for supervised and unsupervised learning are covered, including regression, classification, neural networks, tree learning, kernel methods, clustering, dimensionality reduction, ensemble methods, and large-scale ML.