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| DESCRIPTION | Data science changes the way people process data in different areas. It has promoted the development of many subjects. This course introduces beginners to the whole lifecycle of data science problems and solutions. The course will help students comprehensively understand the basic knowledge of data science and use computer techniques to handle the real-life data science problems. Topics covered include: data collection and processing, computer-oriented modelling techniques, relationship with other mathematics, and case studies. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6279) | We 01:30PM - 04:20PM | Rm 149, E1 | LI, Jia LI, Lei WANG, Wei | 60 | 48 | 12 | 0 | |
| LA01 (6280) | Fr 05:00PM - 05:50PM | Rm 102, W4 | LI, Jia LI, Lei WANG, Wei | 60 | 48 | 12 | 0 |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | This course introduces core data structure and algorithms, which are fundamental to data science and analytics. As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6281) | Th 04:30PM - 07:20PM | Rm 101, W1 | TANG, Jing | 60 | 59 | 1 | 0 | |
| LA01 (6282) | Tu 05:00PM - 05:50PM | Rm 101, W1 | TANG, Jing | 60 | 59 | 1 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6055) | Mo 03:00PM - 05:50PM | Rm 122, E1 | LI, Jia | 60 Quota/Enrol/Avail DSA students including 1-3 co: 60/40/20 | 40 | 20 | 0 | This section is for MPhil(DSA) /PhD(DSA) students only. |
| L02 (6056) | Tu 09:00AM - 11:50AM | Rm 101, E1 | WANG, Wei | 60 Quota/Enrol/Avail For MSc(DCAI) Students Only: 60/7/53 | 7 | 53 | 0 | This section is for MSc(DCAI) students only. |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | A recent trend in Data Science and Machine Learning communities is to further boost the accessibility of Machine Learning techniques by reducing the tedious effort on learning model selection, hyper-parameter tuning, etc. Automatic Machine Learning (AutoML) aims to reduce this tedious effort and make Machine Learning easier to use. In this course, students will master the basics of AutoML, and understand key techniques including hyper-parameter optimization, feature engineering and meta-learning. This course also introduces common AutoML systems and covers real-world case studies on the applications of AutoML. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6068) | Mo 09:00AM - 11:50AM | Rm 150, E1 | WEN, Zeyi | 40 | 39 | 1 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6058) | Tu 09:00AM - 11:50AM | Rm 101, W1 | TANG, Jing | 70 | 55 | 15 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will teach students data science computing techniques. Topics cover: (1) Basic concepts of Data Science Computing and Cloud; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop; and (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6059) | Tu 09:00AM - 11:50AM | Rm 122, E1 | LUO, Yuyu | 60 | 45 | 15 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6061) | Tu 03:00PM - 05:50PM | Rm 102, E4 | ZENG, Wei | 60 | 55 | 5 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | In this course, we will introduce spatial and multimedia database management concepts, theories and technologies, from data representation, indexing, fundamental operations to advanced query processing. Challenges and solution for high dimensional data will also be introduced. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6062) | Fr 01:30PM - 04:20PM | Rm 201, E1 | LI, Lei | 40 | 13 | 27 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This graduate-level course is designed for multi-discipline students to learn and apply industrial analytics techniques for problem-solving and process improvement in various industries. The course will emphasize the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology, along with other data-driven techniques for process improvement and problem-solving. Students will gain hands-on experience through case studies, assignments, and a term project. Prerequisites: Basic knowledge of statistics, data analysis, and engineering principles. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6069) | Th 09:00AM - 11:50AM | Rm 201, W2 | TSUNG, Fu-Gee | 30 | 19 | 11 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Genetics and genomics are closely related fields, both analyzing massive biological data. Genetics studies specific genes and their roles in heredity, while genomics encompasses the entire genome of an organism to understand its complex systems. This course aims to introduce traditional computational biology and revolutionary advancements in machine learning that are transforming the fields of genetics and genomics. It begins with a very brief overview of conventional algorithms in genetics and genomics, setting the stage for a deeper exploration of how genetics and genomics have evolved with the advent of machine learning techniques. As the course progresses, students will be introduced to the latest machine learning methods, with an emphasis on deep learning, which has dramatically changed the landscape of genomics research. Through a combination of theoretical instruction and hands-on teamwork projects, this course is designed to equip students with the necessary skills to navigate and contribute to the field of genomics, leveraging machine learning to uncover new insights into genetic data and its implications for human health and disease. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6072) | Tu 03:00PM - 05:50PM | Rm 101, W4 | ZHANG, Yanlin | 30 | 16 | 14 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Nowadays, the integration of machine learning (ML) into various applications has brought unprecedented opportunities as well as challenges. Machine Learning Security and Privacy is a comprehensive course designed to address the critical need for understanding and mitigating the security and privacy risks inherent in ML systems. The course begins by examining foundational concepts in machine learning and its applications across different domains. It then delves into the specific vulnerabilities and threats that arise in ML systems, such as adversarial attacks, data poisoning, model inversion, and membership inference. Furthermore, the course emphasizes the ethical considerations surrounding ML security and privacy, including the impact of biased datasets, algorithmic fairness, and responsible AI practices. By the end of the course, students will have acquired a deep understanding of the security and privacy challenges in machine learning, along with practical skills to design, implement, and evaluate secure ML systems. This course equips students with essential knowledge to navigate the complex landscape of ML security and privacy effectively. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6074) | Tu 03:00PM - 05:50PM | Rm 202, W4 | HE, Xinlei | 30 | 17 | 13 | 0 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| DESCRIPTION | In this course, an independent research project will be carried out under the supervision of a faculty member. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6076) | TBA | TBA | TBA | 20 | 12 | 8 | 0 |
| VECTOR | [1-0-6:3] |
|---|---|
| DESCRIPTION | This course will teach students practical programming and parallel processing skills on implementing various deep learning or machine learning models, starting from preparing data, feature selection to model choosing, hyperparameter tuning, and final result analysis and explaining. This course is only available for MSc(DCAI) students. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6064) | 02-SEP-2024 - 13-SEP-2024 Tu 01:30PM - 02:20PM | Rm 147, E1 | LUO, Yuyu | 60 | 45 | 15 | 0 | |
| 16-SEP-2024 - 06-DEC-2024 Tu 01:30PM - 02:20PM | Rm 149, E1 | LUO, Yuyu | ||||||
| LA01 (6065) | We 09:00AM - 11:50AM | Rm 147, E1 | LUO, Yuyu | 60 | 45 | 15 | 0 | This class section has two classrooms E1147 and E1228. Please check with the instrucor on the specific location of each class meeting. |
| We 09:00AM - 11:50AM | Rm 228, E1 | LUO, Yuyu | ||||||
| Th 09:00AM - 11:50AM | Rm 228, E1 | LUO, Yuyu | ||||||
| Th 09:00AM - 11:50AM | Rm 147, E1 | LUO, Yuyu |
| VECTOR | [0 credit] |
|---|---|
| DESCRIPTION | In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state of the art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6066) | We 11:00AM - 11:50AM | Rm 102, E4 | TANG, Nan | 120 | 120 | 0 | 0 |
| VECTOR | [2 credits] |
|---|---|
| DESCRIPTION | In this course, students will be trained in the industry. They will work under the guidance of their supervisors (industry and academia) to practice what they have learned in the program, and apply the data science and AI knowledge and techniques to various real-life problems. In Part I, students are required to complete an Open Topic report with oral examination on the scientific value, feasibility and technical challenges of the proposed topic. This course is only available for MSc(DCAI) students. Graded PP, P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6471) | TBA | TBA | LUO, Yuyu | 60 | 53 | 7 | 0 |
| DESCRIPTION | Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6077) | TBA | TBA | TBA | 999 | 62 | 937 | 0 |
| DESCRIPTION | Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6079) | TBA | TBA | TBA | 999 | 19 | 980 | 0 |