Objective & Syllabus
Student will work independently under the supervision of a faculty member on a research and development project in Electronic Engineering. The topic and scope of the study is to be agreed between the student and the supervisor. A project report is required at the end of the course.

Learning Outcome
- To gain advanced knowledge through investigating a topic of a research and development nature.
- To develop competency, aptitude and attitude in conducting rigorous engineering research.

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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
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 sessions where students can have experience with neuromorphic hardware fabrication.

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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