Course Description:

Machine learning is a fast-growing field in computer science and deep learning is the cutting edge technology that enables machines to learn from large-scale and complex datasets.  Ethical implications of deep learning and its applications will be covered and the course will focus on how deep neural networks are applied to solve a wide range of problems in areas such as natural language processing, and image processing.  Other applications such as financial predictions, game playing and robotics may also be covered.  Topics covered include linear and logistic regression, artificial neural networks and how to train them, recurrent neural networks, convolutional neural networks, generative models, deep reinforcement learning, and unsupervised feature learning.

Prerequisites: Basic programming skills, e.g., Python is required.

Course Name: Deep learning

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

Selected topics in computer network and systems that are of current interest will be discussed.

Course Name: Topic in computer network and systems

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

Selected topics that are of current interest will be discussed.

Course Name: Topic in computer science

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

This course offers a theoretical overview of selected topics from the interdisciplinary fields of quantum computation and quantum AI.  The scope of the lectures encompasses an accessible introduction to the fundamental concepts of quantum computation.  Importantly, the introduction does not require preliminary knowledge of quantum theory.  Detailed comparisons of computational principles and related phenomena in the classical and quantum domain outline the stark potential and challenges of quantum theory for fundamentally novel algorithms with enhanced processing power.  Thereupon, the theoretical capability of quantum computers is illustrated by analyzing a selection of milestone algorithms of quantum computation, and their potential applications to artificial intelligence.

Course Name: Quantum computing and artificial intelligence

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

Selected topics in artificial intelligence that are of current interest will be discussed.

Course Name: Topic in artificial intelligence

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

This course introduces basic concepts, technologies, and applications of the Internet of Things (IoT), with a focus on smart sensing.  The course features various topics on sensors and sensing techniques that enable ubiquitous sensing intelligence for IoT devices, and connects them to exciting applications in smart homes, healthcare, security, etc.  The lectures introduce topics like localization, mobile sensing, wireless sensing, acoustic sensing and their applications.

Course Name: Artificial intelligence of things

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

The course will focus primarily on human genomics and medical applications, but the techniques will be broadly applicable across all species.  The topics will include 1) bioinformatics big data analytics and algorithms for sequence alignment and sequence assembly, 2) bioinformatics tasks such variant identification and annotation, gene expression and regulation, and 3) real-life bioinformatics applications such as personal genome analysis and cancer genomics.

Course Name: Introduction to bioinformatics

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

Natural language processing (NLP) is the study of human language from a computational perspective.  The course will be focusing on machine learning and corpus-based methods and algorithms.  We will cover syntactic, semantic and discourse processing models.  We will describe the use of these methods and models in applications including syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization.  This course starts with language models (LMs), which are both front and center in natural language processing (NLP), and then introduces key machine learning (ML) ideas that students should grasp (e.g. feature-based models, log-linear models and then the neural models).  We will land on modern generic meaning representation methods (e.g. BERT/GPT-3) and the idea of pretraining / finetuning.

Course Name: Natural language processing

Course Stream: Other Courses

IsCEF: No

Course Credit: 6