Programming Oracle

# Master of Artificial intelligence - MAI

Courses Page

Artificial Intelligence courses

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# Course Requirements

Complete 180 points

# Part 1 – Complete 120 points in:

# AIML 430

# AIML 430 - Applications and Implications of Artificial Intelligence

This course looks at the range of applications of artificial intelligence in the world of today and the future. It surveys the kinds of problem that can be solved with AI technology and techniques and considers the implications and consequences of using AI technology in these applications. It will discuss the positive and negative outcomes and the ethical issues and principles that need to be considered when creating technological solutions using AI.

# Three further courses from AIML 425-440

# AIML 425 - Neural Networks and Deep Learning

This course addresses the fundamentals of neural network based deep learning. It covers the commonly used deep learning architectures such as fully connected networks, resnets, variational autoencoders, and generative adversarial networks. It discusses functional blocks such as convolutional nets, recurrent neural nets such as LSTMs, and the common objective functions and regularization procedures. Examples will discuss applications such as object classification, classification of sequential text, and the generation of realistic human faces.

# AIML 426 - Evolutionary Computation and Learning

This course addresses evolutionary approaches in machine learning and optimisation. The course will cover both evolutionary algorithms and swarm intelligence as well as some other population-based techniques for problem solving. It will include a range of real-world application domains such as classification, regression, clustering and optimisation.

# AIML 428 - Text Mining and Natural Language Processing

This course focuses on text mining and natural language processing. It covers a variety of topics including text representation, document classification and clustering, opinion mining, information retrieval, recommender systems, query expansion, and information extraction.

or

# AIML 427 - Big Data

Big Data refers to the large and often complex datasets generated in the modern world: data sources such as commercial customer records, internet transactions, environmental monitoring. This course provides an introduction to the theory and practice of working with Big Data. Students enrolling in this course should be familiar with the basics of machine learning, data mining, statistical modelling and with programming.

# 30 further points from:

  • AIML 420-489
  • ECEN 422
  • ECEN 430

# AIML 421 - Machine Learning Tools and Techniques

This course addresses the use of machine learning tools and techniques for analysing data and automatically generating applications. The course will explore a range of tools and techniques for classification, regression, image analysis, clustering, text mining, and preprocessing data. It examines the applicability and limitations of the techniques and methods for analysing and evaluating the outcome of using machine learning tools. Students will gain practical experience in applying a range of tools to a range of different problems from different domains.

# ECEN 430 - Advanced Mechatronic Engineering 2: Intelligence and Design

This course provides a guide to advanced techniques in the field of Mechatronics. The course material studies the interaction between hardware, software and communication components as it relates to embedded systems. Robotics are frequently used to illustrate the mechatronic theory. Artificial Intelligence techniques are introduced as a practical method for addressing the complex interactions between the electronic, mechanical and software components. The course is very practically oriented and primarily uses project-based assessments. These include a robotic competition, real-world customer design, industrial design considerations (in collaboration with the School of Design) and cognitive robotics.

# 30 further points from:

  • AIML 400-499
  • SWEN 400-499
  • NWEN 400-499
  • DATA 400-499
  • ECEN 422
  • ECEN 430
  • STAT 432
  • STAT 452

# SWEN 432 - Advanced Database Design and Implementation

This course explores a selection of the following topics: XML Databases, Cloud Databases, Data Warehouse and Object-Relational Databases. It examines features of these advanced database systems and analyses the new applications they facilitate.

This year the course will examine the following two contemporary fields in the database systems area:

  • Cloud NoSQL Databases, and
  • Data Warehousing.

# SWEN 423 - Design: Patterns, Frameworks and Languages

Object-orientation is the basis for many different programming languages, frameworks and programming patterns. This course explores advanced topics in formal design techniques for OO Languages, OO Frameworks and OO Programming Patterns, and connects those formal designs with practical programming examples.

# Part 2 – Complete AIML 501 and AIML 589 (60 points)

# AIML 501 - Research Essay in Artificial Intelligence

An investigation and literature review of an advanced topic in an area of artificial intelligence, reported in a project description and a literature review essay.

# AIML 589 - Research Project

A supervised research project in an area of Artificial Intelligence.

# Some other Courses

# SWEN 431 - Advanced Programming Languages

This course applies a range of advanced contemporary programming languages in current use, covering practical programming skills in the languages as well as their niches and design paradigms. The course will cover languages of present industrial interest, along with design trends of future languages.

# ECEN 405 - Power Electronics

The course covers the theory, design and application of power electronic circuits and the transformation and control of electrical energy.

# SWEN 423 - Design: Patterns, Frameworks and Languages

Object-orientation is the basis for many different programming languages, frameworks and programming patterns. This course explores advanced topics in formal design techniques for OO Languages, OO Frameworks and OO Programming Patterns, and connects those formal designs with practical programming examples.

# SWEN 426 - Advanced Software Implementation and Development

This course begins by covering issues relating to the successful implementation of a software design, including processes, metrics, the choice of programming language, the choice of implementation tools, coding styles, code reviews, and testing. The course also looks closely at the maintenance stage of softward development, and the issue of quality throughout the entire development process. Issues such as software quality assurance, configuration management and software process improvement are raised.

# SWEN 434 - Data Warehousing

This course considers theory, design and implementation of Data Warehouses.

# SWEN 432 - Advanced Database Design and Implementation

This course explores a selection of the following topics: XML Databases, Cloud Databases, Data Warehouse and Object-Relational Databases. It examines features of these advanced database systems and analyses the new applications they facilitate.

This year the course will examine the following two contemporary fields in the database systems area:

  • Cloud NoSQL Databases, and
  • Data Warehousing.

# AIML 420 - Artificial Intelligence

This course addresses concepts and techniques of artificial intelligence (AI). It provides a brief overview of AI history and search techniques, as well as covering important machine learning topics and algorithms with their applications, including neural networks and evolutionary algorithms. Other topics include probability and Bayesian networks, planning and scheduling. The course will also give a brief overview of a selection of other current topics in AI.

# AIML 421 - Machine Learning Tools and Techniques

This course addresses the use of machine learning tools and techniques for analysing data and automatically generating applications. The course will explore a range of tools and techniques for classification, regression, image analysis, clustering, text mining, and preprocessing data. It examines the applicability and limitations of the techniques and methods for analysing and evaluating the outcome of using machine learning tools. Students will gain practical experience in applying a range of tools to a range of different problems from different domains.

# AIML 425 - Neural Networks and Deep Learning

This course addresses the fundamentals of neural network based deep learning. It covers the commonly used deep learning architectures such as fully connected networks, resnets, variational autoencoders, and generative adversarial networks. It discusses functional blocks such as convolutional nets, recurrent neural nets such as LSTMs, and the common objective functions and regularization procedures. Examples will discuss applications such as object classification, classification of sequential text, and the generation of realistic human faces.

# AIML 428 - Text Mining and Natural Language Processing

This course focuses on text mining and natural language processing. It covers a variety of topics including text representation, document classification and clustering, opinion mining, information retrieval, recommender systems, query expansion, and information extraction.

# AIML 426 - Evolutionary Computation and Learning

This course addresses evolutionary approaches in machine learning and optimisation. The course will cover both evolutionary algorithms and swarm intelligence as well as some other population-based techniques for problem solving. It will include a range of real-world application domains such as classification, regression, clustering and optimisation.

# Approach Focused on the Robot

# Part 1 – Complete 120 points in:

# AIML 430

# AIML 430 - Applications and Implications of Artificial Intelligence

This course looks at the range of applications of artificial intelligence in the world of today and the future. It surveys the kinds of problem that can be solved with AI technology and techniques and considers the implications and consequences of using AI technology in these applications. It will discuss the positive and negative outcomes and the ethical issues and principles that need to be considered when creating technological solutions using AI.

# ECEN 405 - Power Electronics

The course covers the theory, design and application of power electronic circuits and the transformation and control of electrical energy.

# ECEN 430 - Advanced Mechatronic Engineering 2: Intelligence and Design

This course provides a guide to advanced techniques in the field of Mechatronics. The course material studies the interaction between hardware, software and communication components as it relates to embedded systems. Robotics are frequently used to illustrate the mechatronic theory. Artificial Intelligence techniques are introduced as a practical method for addressing the complex interactions between the electronic, mechanical and software components. The course is very practically oriented and primarily uses project-based assessments. These include a robotic competition, real-world customer design, industrial design considerations (in collaboration with the School of Design) and cognitive robotics.

# Three further courses from AIML 425-440

# AIML 425 - Neural Networks and Deep Learning

This course addresses the fundamentals of neural network based deep learning. It covers the commonly used deep learning architectures such as fully connected networks, resnets, variational autoencoders, and generative adversarial networks. It discusses functional blocks such as convolutional nets, recurrent neural nets such as LSTMs, and the common objective functions and regularization procedures. Examples will discuss applications such as object classification, classification of sequential text, and the generation of realistic human faces.

# AIML 426 - Evolutionary Computation and Learning

This course addresses evolutionary approaches in machine learning and optimisation. The course will cover both evolutionary algorithms and swarm intelligence as well as some other population-based techniques for problem solving. It will include a range of real-world application domains such as classification, regression, clustering and optimisation.

# AIML 428 - Text Mining and Natural Language Processing

This course focuses on text mining and natural language processing. It covers a variety of topics including text representation, document classification and clustering, opinion mining, information retrieval, recommender systems, query expansion, and information extraction.

or

# AIML 427 - Big Data

Big Data refers to the large and often complex datasets generated in the modern world: data sources such as commercial customer records, internet transactions, environmental monitoring. This course provides an introduction to the theory and practice of working with Big Data. Students enrolling in this course should be familiar with the basics of machine learning, data mining, statistical modelling and with programming.

# Three further courses from AIML 425-440

# AIML 421 - Machine Learning Tools and Techniques

This course addresses the use of machine learning tools and techniques for analysing data and automatically generating applications. The course will explore a range of tools and techniques for classification, regression, image analysis, clustering, text mining, and preprocessing data. It examines the applicability and limitations of the techniques and methods for analysing and evaluating the outcome of using machine learning tools. Students will gain practical experience in applying a range of tools to a range of different problems from different domains.

# 30 further points from:

  • AIML 400-499
  • SWEN 400-499
  • NWEN 400-499
  • DATA 400-499
  • ECEN 422
  • ECEN 430
  • STAT 432
  • STAT 452

# SWEN 432 - Advanced Database Design and Implementation

This course explores a selection of the following topics: XML Databases, Cloud Databases, Data Warehouse and Object-Relational Databases. It examines features of these advanced database systems and analyses the new applications they facilitate.

This year the course will examine the following two contemporary fields in the database systems area:

  • Cloud NoSQL Databases, and
  • Data Warehousing.

# SWEN 423 - Design: Patterns, Frameworks and Languages

Object-orientation is the basis for many different programming languages, frameworks and programming patterns. This course explores advanced topics in formal design techniques for OO Languages, OO Frameworks and OO Programming Patterns, and connects those formal designs with practical programming examples.

# ECEN 301 - Embedded Systems

This course details how embedded controllers can be used to solve a number of real-world engineering problems. The main emphasis is on 8-bit microprocessors, logic systems to support them and techniques to interface them with the physical world. Specific topics include microcontrollers, sensors, actuators, signal conditioning, filters, analogue to digital conversion, systems analysis and introductory control. Practical experience is gained through the use of programming a microcontroller in a h

# NWEN 439 - Special Topic: Protocols and Architecture for the Internet of Things

This course introduces the fundamental networking protocols and architectures used in the Internet of Things (IoT). In particular, the course will examine the latest protocols and protocol stacks for low power wireless networking in both short-range and long-range settings. It will include in-depth discussion of protocols and algorithms at various layers of the network stack including medium access control, network, application, as well as security aspects unique to IoT.