Our Bachelor of Information System (Honours) in Artificial Intelligence is aimed to cultivate an Artificial Intelligence (AI) practitioners with the ability to implement various AI models and algorithms that lead to new technology in align with the development of industry revolution. The student will acquire theoretical and practical AI knowledge in analysing, modelling, designing, developing and evaluating AI solutions. This programme covers a few areas of AI including Python programming, machine learning, neural network, applied computer vision and deep learning.
- AI Specialist
Software Analysts and Developers
Machine Learning Researcher
Business Intelligence Developer
Bachelor of Information Systems in AI (Honours)
Duration : 3 years
The aim of this module is to provide students with the skills and knowledge to apply statistical concepts and techniques to generate information for hypothesis testing and decision-making. Topics include data, descriptive statistics, probability distributions, sampling distribution, interval estimation, hypothesis testing, correlation and regression, index number and time series analysis. Software is used in the data analysis.
Introduction to Computational Thinking
This course introduces the concepts of abstraction as modelling and abstraction as encapsulation. Students will learn problem solving process with the aid of computer such as formulating a problem and expressing its solution in such a way that a computer can effectively carry it out. They will also learn the distinctive nature of computational thinking compared to engineering and mathematical thinking. The aim of this course is hence to enable students to derive simple algorithms and code the programs to solve some basic problems.
OOP with Python 1
This course introduces core Python programming basics. The course discusses the fundamental principles of Object-Oriented Programming, as well as in-depth data and information processing techniques. It aims to introduce programming, emphasizing understanding, design and development of applications using object-oriented techniques.
Operating Systems & Computer Networks
This course will introduce students with design and implementation of database systems. Topics include data models, storage models, query languages, storage architectures, indexing and transaction processing.
Neural Network Basics
This course introduces the basic mathematical concepts for understanding nonlinearity, feedback in neural networks and the fundamental techniques and principles of neural computation. The course discusses recurrent networks, recurrent feedback loops, statistical pattern recognition, network dynamics and some common models and their applications.
This course introduces students to algorithms and data structures used to develop deep neural network architectures. This course focuses on the design of algorithms and the rigorous analysis of their efficiency. Topics include the basic definitions and tools of algorithms, dynamic programming, sorting, searching; data structures and their applications; graph and string processing algorithms.
The aim of this module is to provide students with the skills and knowledge to apply discrete mathematics to model and solve problems in computer science, data science and AI. Topics covered include mathematical logic and proofs, set theory, counting and probability, mathematical sequences, recursion and inference and graph theory. Students will learn how to apply the theories to solve simple problems in AI, data science and computer science.
Introduction to Information Technology
In this module students will learn fundamentals of Information Technologies, software, applications and job skills required to enter the IT market. This course provides an introduction to computer and information technologies. It discusses the nature of information, computer hardware, software, communications technology, and computer-based information systems.
The purpose of this module is to equip students with the skills of critical thinking. Student will learn practical techniques for language and reasoning, deductive and inductive reasoning, statistical reasoning and critical reflection and demonstrate the ability to apply these to practical situations.
This module aims to introduce students with basic principles and implementation techniques of distributed database systems and highlite differences between distributed database systems and centralized database systems. The students will be introduced with theoretical and practical aspects of the database technologies, showing the need for distributed database technology to tackle deficiencies of the centralized database systems and finally introducing the concepts and techniques of distributed database including principles, architectures, design, implementation and major domain of application.
Software Specification & Modelling
This module aims to introduce students with the concepts, principles, and techniques related to software specification and modelling. In this module students will learn about engineering methods for software specification, tools, skills, and techniques, for software modelling, validation and design. The focus will be on a principled, object-oriented process for software modelling and analysis.
This course introduces the basic approaches and technologies in data science. This course will prepare students to gather, describe, and analyze data, and use advanced statistical tools and programming languages. Topics include probability, statistics, hypothesis testing, regression, clustering, decision trees, and forecasting.
Introduction to Image Processing
This course introduces image processing techniques and theory behind the techniques, as well as practical implementations of them. In this course students will learn fundamental image processing algorithms, and mathematical description of image processing techniques. Topics will include elements of digital image processing, Image models, Sampling and quantization, Relationships between pixels, Components in Digital Image Processing, Basic Image Processing Operations
Statistics for AI
The aim of this module is to provide students with the skills and knowledge to apply advanced statistical methods to solve simple problems in AI. Topics covered include time series analysis and forecasting, Bayesian statistics, Markov chains and experimental design.
Bachelor of Fashion Design (Honours)
Duration : 1 year
Neural Networks for Machine Learning
This course introduces the basic architecture of Neural Networks, Neural Networks learning procedures, Hopfield nets and Boltzmann machines. Students will learn how Neural Networks are used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion.
Deep Learning Fundamentals
This course introduces the fundamentals of deep learning, deep learning models, platforms and libraries. In this course students will learn about applications of deep learning, supervised and unsupervised deep learning models, the basics about deep neural networks, different deep learning models, and how to build deep learning models.
Artificial Intelligence Methods
This course introduces model-based reasoning, reasoning frameworks, Bayesian networks and decision graphs. Students will learn about general principles for artificial intelligence (AI) and efficient representation of uncertain knowledge and understand decision making principles and Learning adaptive systems. Topic will include: Definition of symbol, types of inference, Expert systems, Probabilistic Reasoning System, Logical Reasoning System and Making Simple and Complex Decisions.
OOP with Python 2
This course introduces advaced Python programming skils. The course discusses the advance principles of Object-Oriented Programming, as well as web, database and GUI(Graphical user interface) programming techniques. Topic will include a brief overview of library modules that are used to process common Internet data formats such as HTML, XML, and JSON, CGI scripting, the WSGI interface, and implementing custom HTTP servers. The course will cover high-level library modules that allow Python to connect to standard Internet and web-related services (e.g., HTTP, FTP, XML-RPC).
Deep Neural Network Architectures
This course introduces the details of deep learning architectures with a focus on learning end-to-end models. The course covers feedforward networks, convolutional networks, recurrent and recursive networks, as well general topics such as input encoding and training techniques. The course also provides acquaintance with tools available for building and training deep neural networks.
This module provides the students an understanding of the critical importance of user interface and user experience design. Topics covered will include the industry-standard methods on how to approach the design of a user interface, and the key theories and frameworks that underlie the design of most interfaces use today. The module aims to provide the working knowledge to the students on the principles and practice of UI/UX design.
Natural Language Processing
This course introduces basic techniques in Natural Language processing, automated content analysis and language engineering. Students will learn language translation approaches, filter junk email, extract social networks from the web, and find the main topics in the day’s news, neural network for language phenomena, neural network for automatic discovery of different word senses and phrase structure processing.
The aim of the project module is for students to successfully complete a practical AI project on time and to the required quality standard. The project provides an opportunity for students to define and to manage a self-contained task, which requires the use of cognitive, and project management skills. Students will apply concepts and principles learned in other courses to real situations. The project is completed in two parts: Project Ia and Project Ib. Students must pass both parts. Project, Ia involves the research investigation, requirements analysis and concept design.
The aim of the project module is for students to successfully complete a practical AI project on time and to the required quality standard. The project provides an opportunity for students to define and to manage a self-contained task, which requires the use of cognitive, and project management skills. Students will apply concepts and principles learned in other courses to real situations. Project, Ib involves the proposal preparation, development and presentation. Project IIa and IIb building the AI prototype, making recommendations and writing the project report.
Intelligent Agents Design
This module explores the topic of intelligent agents. It discusses the differences between agents and conventional computer programs, investigates different types of agent architectures, and examines various important aspects and applications of intelligent agents in more detail. The students will learn and understand important problems, challenges, concepts and techniques dealing with the use of intelligent agents for computational tasks.
This course introduces data visualization techniques for design visual data representation and show how to apply these based on the data available and tasks to be achieved. This course includes data modeling, data processing (such as aggregation and filtering), mapping data attributes to graphical attributes, and strategic visual encoding based on known properties of visual perception as well as the task(s) at hand. Students will also learn to evaluate the effectiveness of visualization designs, and think critically about each design decision, such as choice of color and choice of visual encoding. Students will create their own data visualizations, and learn to use Open Source data visualization tools.
Applied Computer Vision
This course introduces concepts, algorithms, and practices of computer vision that are widely used for solving challenging problems. The topics include image processing, feature detection/matching, segmentation, image alignment and stitching, structure from motion, recognition, and tracking. A key focus of the course is on effective implementation of solutions to practical computer vision problems in a variety of environments using both bespoke software authored by the students and standard computer vision libraries.
Cross Cultural Communication
The module focuses on culture and management, organisation and communication with greater emphasis on cross-cultural concepts which contains key ideas from leading theorists, thinkers and practitioners.
This course introduces symbolic knowledge representation in a form suitable for automated reasoning, and associated reasoning methods. It combines formal algorithmic analysis with a description of recent applications. The aims of the course are to introduce key concepts of knowledge representation and its role in artificial intelligence, enable students to design and apply knowledge-based systems, and understand the limitations and complexity of algorithms for representing knowledge and reasoning.
Bachelor of Fashion Design (Honours)
Duration : 1 year
OOP with Python 3
This course introduces depth understanding and advanced usage of Python programming skills. The course discusses the advance principles of data visualization, machine learning, Natural Language processing and neural network libraries application in Python. Topics include Sckit-learn and Linear Regression, Logistic Regression, Decision Tree, Neural networks programming and Python libraries for neural network creation.
Deep Learning with Computer Vision
This course introduces deep learning architectures for the computer vision problems. The students will learn visual representations techniques for common computer vision tasks including matching, retrieval, classification, and object detection. The course discusses deep learning methods from low-level description and futures extraction. Focus will be on Convolution, cross-correlation, linearity, equivariance, weight sharing. Feature maps, matrix multiplication, 1×1 convolution. Padded, striped, dilated convolution. Pooling and invariance. Convolutional networks: LeNet-5, AlexNet, ZFNet*, VGG, NiN*, GoogLeNet.
IS Strategy, Management & Acquisition
In this module students will learn to manage information systems functions in organizations and how it integrates, supports, and enables various types of organizational capabilities. The course addresses issues relating to defining the high-level IS infrastructure and the systems that support the operational, administrative and strategic needs of the organization.
Entrepreneurship and New Business Development
This course is designed to provide students the theoretical concepts and practical tools for entrepreneurship and new business development. It aims to encourage students’ interest in starting own firm, and also to provide the skills and knowledge in managing, influencing and stimulating entrepreneurial activities. Students will engage in the entrepreneurial process, from the idea generation stage to the collection of information, further refinement of the original idea to produce business plan and idea pitching. Student will develop competencies, techniques, and skills about entrepreneurship, set within a reflective and critical approach to study business development in the modern economy.
Ethics and IT
The aim of this module is to provide students with the knowledge and skills to make ethical decisions regarding the application of Information Technology. The approach is from a professional ethics viewpoint. Students will learn what it means to be a professional and how to exercise ethical and moral behavior in their work and daily lives. The module is a mixture of theory and seminars.
Leadership and Innovations
This module introduces students to traditional and modern concepts of leadership, and focuses on skill and knowledge development and organizational operations, effectiveness and competition, and social responsibilities.
Law for the Creative Industries
The aim of this subject is to examine the legal environment in which local and international contemporary design-oriented professionals operate. It is designed to provide students with knowledge of the basic legal principles pertinent to Malaysian, and more generally common law systems. The module focuses on contract law and dispute resolution, duty and standard of care, tort and negligence, copyright, and professional liability and design.
The aim of this course is to provide students with exposure to the actual working envioment by placing them to work in organization outside the univeristy. In addiition, the course enables the student to apply concepts and theories acquired during lectures/classes to the actual practices in applied AI. It is important to note that ALL students must complete an internship during their education experience with Raffles. This is one of the core points of distinction for all of our qualifications.
The aim of the project module is for students to successfully complete a practical AI project on time and to the required quality standard. The project provides an opportunity for students to define and to manage a self-contained task, which requires the use of cognitive, and project management skills. Students will apply concepts and principles learned in other courses to real situations. The project is completed in two parts: Project IIa and Project IIb. Students must pass both parts. Project 1a and 1b, involves the research investigation, proposal, requirements analysis and concept design. Project IIa building the AI prototype.
The aim of the project module is for students to successfully complete a practical AI project on time and to the required quality standard. The project provides an opportunity for students to define and to manage a self-contained task, which requires the use of cognitive, and project management skills. Students will apply concepts and principles learned in other courses to real situations. The project is completed in two parts: Project IIa and Project IIb. Students must pass both parts Project IIa building the AI prototype, Project IIb isto conduct prototype usability testing and writing report.