43023 Emerging Topics in Artificial Intelligence

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Subject handbook information prior to 2025 is available in the Archives.

Recommended studies:

Experiences with an integrated development environment such as Anaconda with Python would be an advantage.

Description

This subject provides a general introduction to emerging topics in artificial intelligence (AI). Targeting emerging topics in AI, such as machine learning, computer vision, natural language processing, search, knowledge representation, inference and reasoning, this subject highlights not only the concepts, definitions, and algorithms of these topics, but also their practical scenarios and industry applications. It is suitable for students who are enthusiastic about AI and motivated to conduct either academic research in AI or develop AI applications in the public and private sectors.

Students have the opportunity to gain a general understanding of state-of-the-art AI topics and develop in-depth knowledge and skills in a set of cutting-edge AI topics through research seminars and practical tutorials supervised by UTS AI leaders and passionate researchers. Students are required to demonstrate their learning outcomes through reflections, technical implementations, and an oral presentation.

Subject learning objectives (SLOs)

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)
• Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.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

This subject will have two 1.5-hour face-to-face classes each week for 12 weeks. The classes encompass guest seminars, and AI technique implementations with lecture and tutorial classes . Guest seminars will introduce students to fresh and cutting-edge AI developments and trends, delivered by UTS AI leaders. AI technique implementations will be directly supervised by established AI researchers in related fields. During the implementation classes, a lecture will explain a designated AI technique, followed by opportunities for students to discuss the technique and its industry applications and frontiers. In the tutorial classes, students will learn about how to implement related algorithms.

In addition to reflections and oral presentations, students are required to explore emerging AI topics with both breadth and depth, developing hands-on skills in algorithm implementation and development.

Content (topics)

Seminar Topics

1. Autonomous Machine Learning
2. Human and AI Collaboration (Brain Computer Interface)
3. AI and Intelligent Drones
4. Reinforcement Learning and Language Models

Implementation Topics

1. Computer Vision and Machine Intelligence
2. Large-scale Data and Graphs
3. Cross-domain Transfer Learning
4. Natural Language Processing & Large Language Models

Assessment

Assessment task 1: Reflections

Assessment task 2: Algorithm Implementation

Assessment task 3: Presentations

Minimum requirements

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

Required texts

1. UTS APA Referencing style
2. Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., & Zhang, G. (2015). Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 80, 14-23.
3. Lin, C. T., Ko, L. W., Chang, M. H., Duann, J. R., Chen, J. Y., Su, T. P., & Jung, T. P. (2010). Review of wireless and wearable electroencephalogram systems and brain-computer interfaces–a mini-review. Gerontology, 56(1), 112-119.
4. McCune, R. R., Weninger, T., & Madey, G. (2015). Thinking like a vertex: a survey of vertex-centric frameworks for large-scale distributed graph processing. ACM Computing Surveys, 48(2), 1-39.
5. Altawy, R., & Youssef, A. M. (2016). Security, privacy, and safety aspects of civilian drones: A survey. ACM Transactions on Cyber-Physical Systems, 1(2), 1-25.
6. Zhang, Y., Wu, M., Tian, G. Y., Zhang, G., & Lu, J. (2021). Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowledge-Based Systems, 222, 106994.

Recommended texts

Note: This list offers an extensive collection of high-quality papers in line with emerging topics in AI. These papers are mainly published in top-level AI journals or conferences.

1. Lu, J., Zuo, H., & Zhang, G. (2019). Fuzzy multiple-source transfer learning. IEEE Transactions on Fuzzy Systems, 28(12), 3418-3431.
2. Zuo, H., Lu, J., Zhang, G., & Pedrycz, W. (2018). Fuzzy rule-based domain adaptation in homogeneous and heterogeneous spaces. IEEE Transactions on Fuzzy Systems, 27(2), 348-361.
3. Zhang, Q., Wu, D., Lu, J., Liu, F., & Zhang, G. (2017). A cross-domain recommender system with consistent information transfer. Decision Support Systems, 104, 49-63.
4. Lin, C. T., & Lee, C. G. (1996). Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice hall.
5. Fumanal-Idocin, J., Takac, Z., Fernandez, J., Sanz, J. A., Goyena, H., Lin, C. T., ... & Bustince, H. (2021). Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface. IEEE Transactions on Fuzzy Systems. DOI: 10.1109/TFUZZ.2021.3092824
6. Do, T. T. N., Lin, C. T., & Gramann, K. (2021). Human brain dynamics in active spatial navigation. Scientific Reports, 11(1), 1-12.
7. Juang, C. F., Chou, C. Y., & Lin, C. T. (2021). Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning. IEEE Transactions on Cybernetics. DOI: 10.1109/TCYB.2020.3041269
8. Lin, C. T., & Do, T. T. N. (2020). Direct-sense brain–computer interfaces and wearable computers. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 298-312.
9. Zheng, Z., Zheng, L., & Yang, Y. (2017). Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3754-3762).
10. Zheng, L., Yang, Y., & Tian, Q. (2017). SIFT meets CNN: A decade survey of instance retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5), 1224-1244.
11. Yu, X., & Porikli, F. (2016). Ultra-resolving face images by discriminative generative networks. In European Conference on Computer Vision (pp. 318-333). Springer, Cham.
12. Li, W., Zhang, Y., Sun, Y., Wang, W., Li, M., Zhang, W., & Lin, X. (2019). Approximate nearest neighbor search on high dimensional data—experiments, analyses, and improvement. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1475-1488.
13. Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R., & Lin, X. (2020). A survey of community search over big graphs. The VLDB Journal, 29(1), 353-392.
14. Wang, X., Zhang, Y., Zhang, W., & Lin, X. (2016). Efficient distance-aware influence maximization in geo-social networks. IEEE Transactions on Knowledge and Data Engineering, 29(3), 599-612.
15. Dong, X., Liu, L., Musial, K., & Gabrys, B. (2021). Nats-bench: Benchmarking nas algorithms for architecture topology and size. IEEE transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3054824
16. Skardinga, J., Gabrys, B., & Musial, K. (2021). Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey. IEEE Access, 9, 79143-79168.
17. Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., & Musial, K. (2020). Multi-level graph convolutional networks for cross-platform anchor link prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1503-1511).
18. Bakirov, R., Fay, D., & Gabrys, B. (2021). Automated adaptation strategies for stream learning. Machine Learning, 110, 1429-1462.
19. Khuat, T. T., & Gabrys, B. (2021). Random Hyperboxes. IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2021.3104896
20. Nguyen, T. D., Musial, K., & Gabrys, B. (2021). Autoweka4mcps-avatar: Accelerating automated machine learning pipeline composition and optimisation. Expert Systems with Applications, 185, 115643.
21. Wu, D., Sharma, N., & Blumenstein, M. (2017). Recent advances in video-based human action recognition using deep learning: A review. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2865-2872).
22. Nag, S., Shivakumara, P., Pal, U., Lu, T., & Blumenstein, M. (2020). A new unified method for detecting text from marathon runners and sports players in video. Pattern Recognition, 107, 107476.
23. Zhang, M., Gao, Y., Sun, C., & Blumenstein, M. (2020). Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy. IEEE Transactions on Image Processing, 30, 150-162.
24. Zhang, Y., Wu, M., Tian, G. Y., Zhang, G., & Lu, J. (2021). Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowledge-Based Systems, 222, 106994.