This eCourse consists of six modules on Artificial Intelligence (AI) & Machine Learning (ML) with AI application.

Module 1 offers an overview of AI and ML, explaining different types of AI, different approaches to the development of AI systems, how ML works, and exploring the types of tasks that AI systems are able to perform.

Module 2 provides an overview of the core mathematical and statistical methods that are important to contemporary data science.

Module 3 provides a high-level overview of different types of data and their uses, benefits, and limitations. While “Big Data” (data collected about people, processes, events, and objects) is being used to fuel artificial intelligence (AI) systems and inform business decisions, other types of data also play an important role in organizations, and many management systems rely on small data and human expertise.

Module 4 provides an overview of Robotic process automation (RPA) and how it can help improve trade processing operations such as settlements, lifecycle management, trade reconciliation, and reporting. RPA is a key tool for reducing errors, minimizing headcount, and increasing efficiency in the process.

Module 5 provides a high-level overview of the Internet of Things (IoT) and its uses and limitations. As smart, connected devices proliferate, the IoT is growing rapidly, transforming businesses across industries. Increasingly, it is having a direct impact on financial services.

Module 6 provides an overview of robo-advice, including its definition, advantages, and regulation. Robo-advice has become an increasingly important tool for wealth management firms. Integrating robo-advice systems can increase efficiencies, lower costs, and open up new markets for wealth management services.

On completion of this course, you will be able to:
- Define functions and list their uses in computing
- Identify and define key mathematical methods, including linear algebra and calculus, and recall their role in computing applications
- Recognize and define important statistical measures and methods and identify their role in advanced business computing applications
- Define Big Data and identify its advantages and disadvantages
- Define small data and list its limitations and uses
- Define expert systems and recall their applications
- Define robotic process automation (RPA) and identify how it can support trade processing
- Define the Internet of Things (IoT) and identify its uses and implications for business
- Define robo-advice and list its advantages and uses in wealth management
- Identify the primary approach to regulating robo-advice

Module 1 - AI & Machine Learning (ML) - Primer

Module 2 - Data Science - Mathematical Methods
Topic 1: Methods Overview
Topic 2: Mathematical Functions
Topic 3: Mathematical Methods
Topic 4: Statistical Methods

Module 3 - Big Data
Topic 1: Data Overview
Topic 2: Big Data
Topic 3: Other Data

Module 4 - AI Applications - RPA in Trade Processing
Topic 1: Trade Processing Overview
Topic 2: Robotic Process Automation

Module 5 - AI Applications - Internet of Things (IoT)
Topic 1: IoT Overview
Topic 2: IoT in Action

Module 6 - AI Applications - Robo-Advice
Topic 1: Robo-Advice Overview
Topic 2: Robo-Advice in Action