Objective
This course aims to provide students with a general understanding of various computational techniques that enable machines to understand different types of multimedia data, including text, speech, image and video. The course content covers the methods that are used to analyze, classify and detect the underlying information, properties and modalities inherent in complex data. Students will learn the theories, models, algorithms and operation of machine learning tools, which have been successfully developed and deployed for speech/audio, image/video, and other multimedia applications. Specifically, the basics and recent progress of machine learning techniques will be introduced.

Syllabus

1. Introduction to the course
2. Mathematical & programming basics
3. Supervised Learning
   • Logistic regression
   • Linear Classifier
   • EM Algorithm
   • Support Vector Machine
4. Unsupervised Learning
   • Principal Component Analysis;
   • ZCA Whitening;
   • Clustering;
5. Basics on Neural Network & Multi-layer Perceptron
6. Convolutional Neural Network
7. Recurrent Neural Network
8. Generative Adversarial Networks
9. Machine learning for different data modalities
   • Feature representations for different modalities
10. Practice on neural networks

Learning Outcome
Upon completion of this course, students will be able to:

• Describe the properties of different types of multimedia data
• Explain the fundamental concepts, theories and algorithms of machine learning techniques
• Describe the advantages, limitations and trends of machine learning and techniques
• Apply machine learning algorithms to solve given problems of various multimedia data
• Use machine learning tools to implement a system of multimedia data processing

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Objective
Driven by the AI boom, technologies such as robotics, virtual reality, and self-driving have begun to emerge, leading to an ever-increasing demand for high-efficiency, data-intensive computing. Inspired by the brain, post-von-Neumann neuromorphic computing was proposed. Mapping the biological neural networks to implement neuromorphic computing requires both algorithm and hardware designs. Although the algorithm approach is being widely exploited, neuromorphic hardware is still in its early stages of development, requiring continual advancements in the underlying materials, devices, and circuits. This course covers the most up-to-date research in the field of neuromorphic hardware. The course will begin with an introduction to neuromorphic computing, followed by discussions on neuromorphic hardware development, in which the students will study the materials, devices, and circuits exploited to enable neuromorphic hardware. In addition to lectures, the course includes lab case study sessions where students can have experience with neuromorphic hardware fabrication.

在人工智能熱潮的推動下，機器人、虛擬現實和自動駕駛等技術開始湧現，對高效、數據密集型的計算提出了不斷增長的需求。受大腦計算模型的啟發，後馮諾依曼的類腦計算方式被提出。映射生物神經網絡以實現類腦計算需要算法和硬件的設計。儘管類腦計算算法被廣泛使用，類腦計算的硬件開發仍處於早期階段，而它的發展依賴於其底層材料、器件和電路的不斷發展。本課程將討論類腦計算硬件設計的最新研究。課程將首先介紹類腦計算的概念，然後詳細討論類腦計算的硬件設計。學生將學習類腦計算硬件設計實現的材料、設備和電路。除了課堂授課，課程還設計了實驗室展示環節，學生可以在實驗室中參觀類腦計算的硬件製備。

Syllabus
Introduction to neuromorphic computing; Review on semiconductors, semiconductor devices and circuits; von Neumann architecture; Hardware implementations of artificial synapses and neurons; Device and circuit design and fabrication for neuromorphic hardware.

Learning Outcome
Upon successful completion of the course, students will be able to

• Be familiar with the von Neumann and non-von Neumann hardware architectures
• Gain the key fundamental knowledge of semiconductors, semiconductor devices and circuits to implement computation
• Apply the fundamental semiconductor knowledge to the design and implementation of the biological synapses and neurons and the neuromorphic hardware
• Well understand neuromorphic hardware development at device and circuit levels

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Objective & Syllabus

This course introduces the key concepts and issues of innovation, technology and management in the context of modern engineering practices. The new wave of socio-technological development is viewed as an integration and convergence of innovations and technology in communication, work, entertainment and others in our daily life. The objective of the course is to provide students a general overview and roadmap of creating cutting edge innovation and evolving digital economy. It helps students to establish a deep understanding about how engineering practice works, and how it affects and reshapes our communities and society, and about how to become best engineering performers. The best practices of intellectual property (IP) rights, protection, enforcement, and IP management from a technology perspective will also be introduced. Through case studies, students will appreciate the decision process on the type of technology to be developed, the development process of the technology, and how to turn new technology into real products. The aspects of funding, market study and commercialization will be covered.

Learning Outcome

Upon completion of this course, students will be able to:

• Appreciate the importance of best engineering practices in innovation and technology development
• Describe the roles of innovation and technology in modern society and economy
• Explain the creation, evolving, marketing and commercialization processes of a new technology
• Make independent judgement on technology need and trends
• Formulate a strategy to exploit a technology with IP protection
• Use IP information for planning and decision making
• Establish an IP management strategy for an organization

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Objective & Syllabus
This course is designed to allow students to acquire a basic understanding and the skills of the practical aspects of the Electronic Engineering profession. During the internship, the student must attach to a company in a study-related position for no less than 12 weeks. The student will have an academic supervisor (as primary supervisor) and an industry co-supervisor from the company, both have the expertise to provide advice to the student. To be qualified for award of the subject credit, the student must submit a report summarizing the internship experience at the end of the internship. Additional presentation may be required by the hosting company. The internship should normally take place in the summer term after a student has finished the first two semesters of studies. Part-time students can decide to undertake the internship in the summer term of either the first or second year of studies. Students are recommended to seek the Professor-in-Charge's comment on potential internship opportunities before enrolling in the course.

Learning Outcome
At the end of the course of studies, a student should be able to

• Have a good understanding and appreciate the characteristics of an Electronic Engineering work environment, including employer expectation, management structure of a team, industrial standards and practices, trends and common issues in the Electronic Engineering, etc.;
• Carry out basic duties in an Electronic Engineering work environment;
• Exhibit good etiquette in the workplace;
• Work independently as well as in a team environment; and
• Communicate effectively and efficiently with peers, supervisors and possibly also clients.

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