Introduction to Artificial Intelligence and Machine Learning

Home Admissions Course Information Introduction to Artificial Intelligence and Machine Learning

COMP 8920SEF

Course Guide
Introduction to Artificial Intelligence and Machine Learning

COMP 8920SEF

Course Guide

Introduction to Artificial Intelligence and Machine Learning

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Course Coordinator:

Dr Au Yeung Siu Kei Jeff, BEng(Hons), MPhil, PhD (HKUST)

Course Developer:

Dr Au Yeung Siu Kei Jeff

COMP 2010SED Computing Fundamentals with Java is a two-term, six-credit-unit, 2000-level course within the Bachelor of Computing with Honours in Internet Technology (BCITH) programme suite and the Bachelor of Science with Honours in Computing and Networking (BSCICNH) programme suite. It is a compulsory course for BCITH and BSCICNH. This course is designed to provide a solid foundation in the fundamentals of computing and is suitable for students who do not have prior experience in programming. The problem solving aspect of computing will be emphasised. The main focus is to learn an object-oriented programming language (Java) and use it to solve problems. The software development cycle will also be introduced.

Advisory Prerequisite(s)

You are strongly advised to have already studied PSYC 2028AED

Excluded Combinations and Courses Not Allowed to be Taken Again

COMP S492F, COMP 3920SEF, COMP S491, COMP 4910SED

Aims

This course aims to introduce the basic concepts, problems, approaches and applications of artificial intelligence and machine learning. The topic includes an overview of AI, machine learning theory and methods, Machine learning in data science, neural networks and deep learning, hardware and software technologies for AI systems, natural language processing, computer vision, AI in games and sports, biomedical intelligence, intelligent manufacturing and robotics, ethical and legal issues with AI, etc.

Contents

The course includes the following main topics:

  • Overview of artificial intelligence
  • Machine learning theory and methods
  • Neural networks and deep learning
  • Computer vision
  • Natural language processing
  • Other applications with AI

Learning support

Tutorials

Assessment

The continuous assessment (50%) consists of lab exercises (25%) and one test (25%). There will be no written examination for this course. The final examination (50%) will be based on an individual project consisting of a presentation and a project report.

Electronic submission of assignments

Except for some designated assignments, students are required to submit assignments via the Online Learning Environment (OLE).

Online requirement

Students are required to submit their assignments and project report via the Online Learning Environment (OLE).

Equipment and Software

A computer or electronic device capable of connecting to the Internet.

Equipment

A PC-compatible computer ready for connecting to the Internet is essential. The minimum configuration of the computer is:

  • PC with an x86 CPU or equivalent
  • 1GB RAM
  • Hard disk
  • Sound card with speakers

You need to be familiar with the basic use of a computer including keyboard, mouse and word processing software. Experience in using email, the Internet and the World Wide Web is also essential.

Software

You should have Windows with Microsoft Word. Other software will be downloaded from the Internet. You may need to use OpenOffice to prepare assignment answers.

Set book(s)

Ng, A. (2018). Machine learning yearning (draft version). https://www.deeplearning.ai/resources/#ebooks

Students with disabilities or special educational needs

The audio and visual components of this course may cause difficulties for students with an audio or visual handicap. You are encouraged to seek advice from the Course Coordinator before enrolling on this course.