43023 Emerging Topics in Artificial Intelligence

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 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 specific AI topic, with an individual project supervised by UTS’s leading AI researchers. Students are required to demonstrate their learning outcomes through reflection, a quiz, a technical implementation, a research report, 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. In each class, the lecturer/guest lecturers will first give a brief introduction to a designated AI topic, then students will have chances to discuss this topic in a Q&A session, as well as a specific tutorial in line with this topic.

An individual project is specifically designed to help students practice experimental skills in implementing related algorithms, reviewing the literature, and presenting results. Together with reflections and online quizzes, students are required to explore the topics with breadth and depth to demonstrate their understanding of their chosen AI topics.

Content (topics)

1. Cross-domain Transfer Learning
2. Brain Computer Interface
3. Computer Vision and Machine Intelligence
4. Large-scale Data and Graphs
5. Anomaly Detection
6. Reinforcement Learning and Imitation Learning
7. Complex Networked Systems
8. Fuzzy Neural Networks
9. Autonomous Machine Learning
10. AI and Intelligent Drones

Assessment

Assessment task 1: Reflections

Assessment task 2: Knowledge Quiz

Assessment task 3: Individual Project

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. Buckley, J. J., & Hayashi, Y. (1994). Fuzzy neural networks: A survey. Fuzzy Sets and Systems, 66(1), 1-13.
5. 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.
6. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58.
7. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.
8. 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.
9. Nunan, D. (1996). Towards autonomous learning: Some theoretical, empirical and practical issues (pp. 13-26). Taking Control: Autonomy in Language Learning. Hong Kong: Hong Kong University Press.
10. 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.
11. 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 and are mainly contributed by invited speakers of this subject. Any questions and interests on these papers, you are welcome to contact the authors directly.

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. Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2041-2050).
16. Yin, H., Wang, W., Wang, H., Chen, L., & Zhou, X. (2017). Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 29(11), 2537-2551.
17. Pang, G., Cao, L., & Chen, L. (2016, January). Outlier detection in complex categorical data by modelling the feature value couplings. In IJCAI International Joint Conference on Artificial Intelligence.
18. Li, W., Duan, L., Xu, D., & Tsang, I. W. (2013). Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE transactions on Pattern Analysis and Machine Intelligence, 36(6), 1134-1148.
19. Liu, W., & Tsang, I. W. (2017). Making decision trees feasible in ultrahigh feature and label dimensions. Journal of Machine Learning Research, 18, 1-36.
20. Xu, D., Shi, Y., Tsang, I. W., Ong, Y. S., Gong, C., & Shen, X. (2019). Survey on multi-output learning. IEEE transactions on Neural Networks and Learning Systems, 31(7), 2409-2429.
21. Dong, X., Liu, L., Musial, K., & Gabrys, B. (2021). Nats-bench: Benchmarking nas algorithms for architecture
22. topology and size. IEEE transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3054824
23. 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.
24. 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).
25. Bakirov, R., Fay, D., & Gabrys, B. (2021). Automated adaptation strategies for stream learning. Machine Learning, 110, 1429-1462.
26. Khuat, T. T., & Gabrys, B. (2021). Random Hyperboxes. IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2021.3104896
27. Nguyen, T. D., Musial, K., & Gabrys, B. (2021). Autoweka4mcps-avatar: Accelerating automated machine learning pipeline composition and optimisation. Expert Systems with Applications, 185, 115643.
28. 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).
29. 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.
30. 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.
31. 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.