Course: Professional Diploma in Data Science and Artificial Intelligence

Overview

Course Description

Artificial Intelligence (AI) is a very hot topic, and AI solutions have significantly changed the way we do things across various domains in our life. Today, It is widely recognised, that these solutions are outperforming human’s experts across different industries. Therefore, Artificial Intelligence (AI) adoption is a necessity to maintain a competitive advantage regardless of industry. AI is the science of processing data, often in real-time, to improve productivity and efficiency and make decisions better informed. In the past decade, AI has been the key driver behind many innovations such as autonomous vehicles, image/voice recognition, effective web search and intelligent security systems. This course is designed to give participants the knowledge of AI concepts including the essential technical foundations required for appreciating as well as communicating the value of AI-enabled solutions to the potential or existing clients/customers. During the course, learners will work on a number of case studies from various sectors including government, healthcare and will work with different data modalities (structured and unstructured). You will understand not only the theory, but also how it is applied in specific industries. You will hear from industry leaders in AI sub fields such as deep learning, who will share their personal experiences in adopting or developing AI solutions. This course will help you to find creative ways to apply AI to your work. It will equip you with the knowledge and tools to craft a strategy to implement AI successfully to overcome challenges and meet business needs.

Keywords: AI, Deep Learning Machine Learning, Un-supervised Learning, Python, R, Data Visualisation, Reproducible Documents, Tensorflow, Keras, Hadoop, Spark

Course Learning Outcomes:

· Humans versus machines. Throughout this course you will be able to study and contrast various case studies that compares human’s and computer performances across various domains

· Build strong understanding of AI and underlying concepts and learn about the various tools and technologies available for building and deploying intelligent solutions

· Understand and build the necessary skills that will enable you to articulate and define data-related problems, outline key requirements and resources for building a scalable AI-driven solution and lead the technical implementation

· By the end of this programme you will develop strong understanding of the data-driven application workflow and its requirements

· Develop strong theoretical and technical understanding of traditional machine learning algorithms and modern AI, including Deep Learning and Deep Convolutional Neural Networks

· Build the vision to lead digital transformation in your organisation through a capstone project.

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Learners must complete all Six units to achieve the qualification.

Unit 1: Preliminaries (AI Overview, Tools and Technologies)

Course Description

This course gives an introduction to fundamental concepts of AI and its applications. This course is for non-technical learners with no background knowledge in the subject, and will introduce you to the most common tools and technologies used in this domain Learning outcomes: • Develop strong understanding of Artificial Intelligence, Machine Learning and Data-Driven Applications • Develop strong understanding of various data modalities (Structured and unstructured data) and how it can be utilised intelligently • Skills, and resources required to build an AI strategy • Tools and technologies for implementing AI solutions (from small datasets, to very large and complex data repositories) • Ethical and societal concerns around AI

Unit 2: Intelligent Data-Driven Applications Workflow

Course Description

You will develop strong understanding of the data-science and data-driven applications development workflow. You will understand how to work with real-world problems and develop the necessary skills to understand what technology/s required to solve it. You will learn how to articulate the right questions/problems related to your business/ data, and will be able to outline the necessary components to build an intelligent solution for solving it Learning outcomes • Build the necessary skills to apply different data-visualization and exploration methods to uncover hidden knowledge in wide range of real-world datasets • Understand, and apply various data preparation techniques to prepare your data for intelligent mining (how to handle categorical variables, images, text, and so on) • Learn the Data Science Workflow from data exploration all the way to solution deployment

Unit 3: Machine Learning

Course Description

Machine Learning (ML) is in the heart of most AI-driven applications. In this unit, you will develop strong understanding of the underlying concepts of ML, and will be able to build and evaluate your ML solutions. Learning outcomes: • Develop strong understanding of the key components for building Supervised/ Unsupervised Machine Learning Algorithms. This will include training, validating and testing Machine Learning Solutions • Develop Strong understanding of different machine learning algorithms. You will be able to learn and apply wide range of algorithms to solve different problems. This will include regression models, kernel methods and ensemble-based methods • Describe best practices in machine learning (bias/variance theory; innovation process in machine learning and AI) • Deploy Machine Learning solutions using interactive front-end applications. You will learn about various methods to deploy machine learning solutions to end-users. In this unit, you will build, evaluate and deploy a machine learning algorithm to end-users using advanced technologies.

Unit 4: Deep Learning

Course Description

This course covers modern AI methods using Deep Learning. You will develop strong understanding of Neural Networks, Convolutional Neural Networks and other Deep Learning underlying concepts. A number of case studies from various sectors will be used throughout the course to illustrate the theory and practical aspect of state-of-the-art Deep Learning technologies and frameworks Learning outcomes: • You will develop strong understanding of key differences between Traditional Machine Learning Versus Deep Learning Methods, and will be able to decided when to use each method • You will learn the key concepts of Neural Networks and Deep Learning-based methods • You will develop strong understanding of computer vision using Deep Convolutional Neural Networks • Build and evaluate an end-to-end solution using Deep Learning-based approaches • You will develop strong understanding of the latest research and development in the domain of deep learning, including open research challenges and issues

Unit 5: Legal, Social and Ethical Issues of AI

Course Description

The preponderance of AI leads the emergence of new legal, social and ethical concerns. It is essential to have AI ethicists, lawyers and social experts to help navigate where this technological advance might take us. Many international efforts such as the Draft Ethics Guidelines for Trustworthy AI.9 Pursuant released by the European Commission's High-Level Expert Group on Artificial Intelligence ("AI HLEG”) in 2018 and the Directive on Automated Decision-Making released by the Treasury Board Secretariat of Canada in 2019 set guidelines to ensure that AI driven decision-making is compatible with core legal, administrative, social and ethical principles. This course investigates a number of emerging legal, social and ethical issues associated with the use of AI in general and in government in specific. It equips learners with the knowledge to form views on how to develop strategies to respond to challenges posed by AI application. Learning outcomes: • Describe AI’s impact on human fundamental rights and applicable regulation, as well as core principles and values to ensure transparency, accountability, legality, and procedural fairness. • Understand the legal, social and ethical aspects of AI, and how the law will evolve in response to AI. • Assess how the volume and nature of data collection used in AI may result bias and/or discrimination in AI-enabled systems in their decision-making processes.

Unit 6: Digital Transformation Toward AI-Driven Practices (Individual Project)

Course Description

The key requirements for adopting an AI strategy are to articulate and clearly define a problem and know your data. In this unit, the learners will work independently, to identify a data-related problem such as existing practices, specific challenging tasks, and so on (preferably within their working environments), and outline the key components to transform it into an intelligent practice. The learners will build a complete proposal, that defines and motivates a specific problem, outline the key stages to move toward an AI-driven practices and identify the required resources and associated risks. Under mentorship and supervisor of the instructors the learners will be able to: • Identify a specific problem, and outline the key components such as the availability of data, data quality, privacy, and so on • Discuss the limitation of existing practices related to the above problem • Propose, contrast various AI-driven solutions and choose the right direction to solve the problem and improve the current practice • Outline the key required resources to realise the solution and provide rough estimates of the cost (e.g. software, tools, personnel, and so on) • Lead the technical implementation

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Unit 1: Preliminaries (AI Overview, Tools and Technologies)

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Unit 2: Intelligent Data-Driven Applications Workflow

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Unit 3: Machine Learning

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Unit 4: Deep Learning

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Unit 5: Legal, Social and Ethical Issues of AI

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Unit 6: Digital Transformation Toward AI-Driven Practices (Individual Project)

Course Description

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