2021 Spring Courses from Information Hub
Course Introduction-2021 Spring
AIAA 5023 Foundations of Deep Neural Networks
Instructor: CHEN, Yingcong
Time: Wednesday 10:30-13:20
Introduction: This course helps students to get basic knowledge about deep neural networks, helping them to understand basic concepts, capabilities and challenges of deep neural networks.
AIAA 5026Computer Vision and Its Applications
Instructor: CHEN, Yingcong
Time: Monday 10:30-13:20
Introduction: This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc.
AIAA 5027 Deep Learning for Visual Intelligence: Trends and Challenges
Instructor: WANG, Lin
Time: Tuesday 16:30-17:50, Thursday 16:30-17:50
Introduction: This is a task-oriented yet interaction-based course, which aims to scrutinize the recent trends and challenges in visual intelligence tasks (high- and low-level vision tasks). This course will follow the way of flipped-classroom manner where the lecturer teaches the basics; meanwhile, the students will also be focused on active discussions, presentations (lecturing), and hands-on research projects under the guidance of the lecturer in the whole semester. Through this course, students will be equipped with the capability to critically challenge the existing methodologies/techniques and hopefully make breakthroughs in some new research directions.
CMAA 5002 Animation Art: From Concept to Production
Instructor: ZHANG, Junjie
Time: Monday 13:30-14:50, Friday 9:00-10:20
Introduction: From screenplay through post-production, students are immersed in the collaborative animation pipeline. In this course, students will utilize a variety of animation tools and techniques to tell a compelling story and experience the diverse roles within the animation industry through storyboarding, editing, and completion of a short animated film.
CMAA 5013 Interactive Storytelling
Instructor: HU, Rui & Yip, David
Time: Thursday 15:00-17:50, Thursday 15:00-17:50
Introduction: This course explores interactive storytelling as cinematic, narrative and interactive art forms. It focuses on the art and techniques of visual storytelling that can interact thematically with the audience in meaningful ways. Students will learn about the key aspects of filmmaking and can apply narrative and interactive principles to storytelling in playable media and platform.
CMAA 5015 Introduction to Interactive Art
Instructor: Guljajeva, Varvara
Time: Monday 15:00-16:50, Monday 17:00-17:50
Introduction: This course serves as an introduction to understanding interactive art and the new media art scene in general. The class gives an overview of art history concerning active audience involvement in an artwork: starting from the roots of interactive art (such as Futurism, Dada, Fluxus movements) until the future scenarios in the age of artificial intelligence (AI) and machine learning (ML). Apart from references and history, the course contributes to critical thinking, reflection, contextualization, and understanding different types of audience interaction modes and interfaces. The core principles and methodologies of interaction design will be introduced. In addition to that, the problematics of preservation and also presentation of the interactive artworks in the exhibition space will be addressed.
CMAA 5016 Programming for Computational Media and Arts
Instructor: PAPATHEODOROU, Theodoros
Time: Wednesday 12:00-14:50
Introduction: This course introduces students to programming for computational media and arts. Students learn the principles, algorithms and coding frameworks for creating graphics, sound and interaction with machines while also sharpening their sense of aesthetics. Each week, the relevant computational artworks employing these techniques are introduced, analyzed and discussed.
CMAA 5017 AR/VR/MR/XR: Concepts, Theory and Techniques
Instructor: HUI, Pan & FAN, Mingming
Time: Friday 15:00-17:50
Introduction: This course introduces students to the concepts, theories, and interaction techniques in AR/VR/MR/XR. It covers both the fundamental concepts and design theories and the state-of-the-art interaction techniques in the field. In addition, students will work independently or in teams to design, develop, and evaluate AR/VR/MR/XR prototypes. In sum, by the end of the class, students will have a solid grasp of the fundamental concepts, design principles and research gaps in AR/VR/MR/XR and gain hands-on experience in designing and evaluating AR/VR/MR/XR prototypes.
DSAA 5009 Deep Learning in Data Science
Instructor: WANG, Wenjia
Time: Monday 10:30 11:50, Wednesday 10:30 11:50
Introduction: In this course, theories, models, algorithms of deep learning and their application to data science will be introduced.The basics of machine learning will be reviewed at first, then some classical deep learning models will be discussed, including AlexNet, LeNet, CNN, RNN, LSTM, and Bert. In addition, some advanced deep learning techniques will also be studied, such as reinforcement learning, transfer learning and graph neural networks. Finally, end-to-end solutions to apply these techniques in data science applications will be discussed, including data preparation, data enhancement, data sampling and optimizing training and inference processes.
DSAA 5012 Advanced Database Management for Data Science
Instructor: WANG, Wei
Time: Monday 16:30-19:20
Introduction: In this course, the concepts and implementation schemes in advanced database management systems for data science applications will be introduced, such as disk and memory management, advanced access methods, implementation of relational operators, query processing and optimization, transactions and concurrency control. It also introduces emerging database related techniques for data science.
DSAA 5020 Foundation of Data Science and Analytics
Instructor: TANG, Jing
Time: Wednesday 13:30-16:20
Introduction: 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.
DSAA 5021 Data Science Computing
Instructor: CHU, Xiaowen
Time: Monday 12:00-13:20, Wednesday 12:00-13:20
Introduction: 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.
DSAA 5024 Data Exploration and Visualization
Instructor: ZENG, Wei
Time: Tuesday 18:00-20:50
Introduction: 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.
DSAA 6000B Introduction to Graph Learning
Instructor: LI, Jia
Time: Wednesday 16:30-17:50, Friday 16:30-17:50
Introduction: Graph, as a very expressive model, has been widely used to model real-world entities and their relationships in application-specific networks. In this course, students will gain a thorough introduction to the basics of graph theories, as well as cutting-edge research in deep learning for graphs. The topics include graph embeddings, graph neural networks, graph clustering models, graph generative models, adversarial attacks on graphs, graph reasoning, etc.
IOTA 5102 Localization for IoT
Instructor: GONG, Zijun
Time: Wednesday 9:00-11:50
Introduction: This course introduces students to the fundamentals and latest research on localization for Internet of Things, including GPS, indoor positioning based on ultra-wideband communications, and simultaneous localization and communications in 5G/6G, et al. Apart from electromagnetic waves, the localization based on acoustic signal will also be introduced.
IOTA 5103 Emerging Wireless Technologies for IoT
Instructor: XING, Hong
Time: Monday 13:30-14:50, Friday 9:00-10:20
Introduction: With the proliferation of IoT devices and applications, successful delivery of latency-critical and energy-constrained services pose new challenges for the next-generation wireless communications. In this course, basic principles and techniques for designing wireless systems will first be covered, then followed by introduction to the state-of-the-art of emerging technologies, in which we will investigate sustainable, scalable and smart solutions for IoT.
IOTA 6910A Security in IoT Systems: Theory and Applications
Instructor: LI, Songze & TYSON, Gareth
Time: Thursday 9:00-11:50
Introduction: This course covers fundamental and applied aspects of privacy and security in the Internet of Things (IoT). The course will teach students about the fundamentals of cryptography, equip students with the abilities to rigorously understand and analyse the security of information systems, and get familiar with practical security technologies like private and public key encryption, message authentication, and secure computation. This course will then explain how these technologies are used and deployed in IoT environments, before exploring how recent attacks have discovered new vulnerabilities in real IoT deployments. The course will emphasise the value of empirical observations and give students insight into how these vulnerabilities can be measured in-the-wild.
By the end of the course, students will: (1) Develop a systematic understanding of notion of security in information systems; (2) Understand and be able to evaluate basic cryptographic technologies; (3) Have a solid grasp of how these technologies are deployed, and how we can measure their efficacy in real deployments of IoT systems; and (4) Understand a set of case study vulnerabilities (and defences) that have been discovered in-the-wild.
For more details, please refer to: https://w5.ab.ust.hk/wcq/cgi-bin/2130/subject/AIAA