Page 292 - 2024.2025 - 澳門理工大學學士學位課程手冊 (電子書)

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Code Module Credits Duration Prerequisite(s)
Elective Subjects - Group B (Cont.)

CSAI0115 Data Mining Technology and Application 3 45 hrs CSAI2123
The field of data mining aims at extracting useful and interesting patterns and knowledge
from large data repositories such as databases and the Web. It integrates techniques from
database, statistics and artificial intelligence. This module provides a broad view of this field,
and examines the methods that have proven valuable in recognizing patterns and making
predictions. It also develops students' ability to use data mining techniques for business
decision making.
CSAI0116 A.I. driven Drug Discovery and 3 45 hrs ---
Development
Artificial intelligence (AI) plays an important role in new drug discovery and development.
This module explains how to apply AI techniques in drug discovery and development,
including the prediction of target structure, lead discovery, lead optimization and drug-
likeness evaluation based on the usual AI algorithms. Additionally, it also covers the latest
research progress and successful industrial cases of AI driven drug discovery and
development.
CSAI0117 Advanced Topics in A.I. I 3 45 hrs ---
Computer aided diagnosis (CAD) can be defined as the diagnosis made by the radiologist
supported by a computer based medical image analysis that acts as a second opinion system.
The module aims at giving the students the knowledge and ability to develop image
enhancement, image analysis and classification systems useful in CAD environments.
CSAI0118 Advanced Topics in A.I. II 3 45 hrs ---
Strong AI requires autonomous systems that learn to make the right decisions.
Reinforcement learning (RL) is a powerful paradigm for doing this, and it can be used to a
large number of tasks, including robotics, gaming, consumer modelling, and healthcare. This
module will provide a solid introduction to the field of reinforcement learning, and students
will gain an understanding of core challenges and approaches, including generalization and
exploration. Through a combination of lectures, written and coded assignments, students
will become proficient in the key ideas and techniques of reinforcement learning and deep
reinforcement learning.
CSAI0119 High-performance and Parallel 3 45 hrs ---
Computing
This module covers the principles of High-Performance Computing (HPC) and parallel
computing. The fundamental of parallel programming such as multiprocessing and
multithreading are discussed. Topics include technologies and approaches of computation
using multicore processor, multi-processor; distributed computing and heterogeneous
computing.
CSAI0120 Domain Specific Languages 3 45 hrs ---
Domain-specific language (DSL) is a programming language specifically designed to working
within a particular area of interest. DSLs have become a core part of model-driven software
development. Using a DSL increases productivity for developers and improves their
communication with business experts. This module introduces DSL techniques and discusses
approaches on how to implement such languages in practice. It starts with an overview of
domain-specific languages, both text-based and graphical. A trivial example language is then
discussed and implemented using two special software tools: Eclipse Xtext and JetBrains
MPS.

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