ÐÓ°ÉÂÛ̳

From chatbots, personalised recommendations on social media, traffic predictions and virtual personal assistants including Siri and Alexa, advances in machine learning are becoming an integral tool that help individuals navigate the modern world.

Increased adoption of such technology across the world has driven massive growth in the volume of data, requiring businesses to harness the power of machine learning to make decisions, learn about and predict customer behaviour to drive strategic advantage.

Combining the fields of engineering, statistics, mathematics and computing, machine learning is one of the leading data science methodologies revolutionising business. This course will cover a wide range of machine learning methods, both model-based and algorithmic. Presenting the theoretical foundations of these methodologies, you will have an opportunity to apply them using real-world examples and datasets.

Computer seminars that enable you to practice your programming skills and a course project give you the opportunity to explore how machine learning can be used innovatively to solve pressing business challenges such as algorithmic trading in the financial industry, predicting customer behaviour, and improving compliance and risk management. By the end of the course, you will have developed the ability to understand how machine learning can be integrated into current business models and the challenges that this poses.

Prerequisites: You should have completed at least one semester of calculus, and at least one semester of probability and statistics to undertake this course. Some minimal experience with computer programming is also required.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: An individual project (50%) and a two-hour written exam (50%)

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is designed for students from various disciplines that use data to inform decision-making. It is suitable if you want an in-depth understanding of how machine learning can be integrated into modern business.

You should also consider taking this course if you are targeting a career in IT, data science, marketing, research, consulting and business management. It is equally suited if you are a professional already working in industry, government, or research organisations, looking to develop your understanding of this rapidly developing field.

• Show in-depth knowledge of supervised and unsupervised machine learning algorithms
• Learn to perform some of the main techniques and algorithms for regression, classification, tree-based methods and graphical models in R
• Discuss the application of clustering and principal component analysis, neural networks and deep learning at an introductory level
• Develop an understanding of the process to learn from data and inform decisions on real-world problems
• Apply and evaluate suitable methods to various datasets by model selection and predictive performance assessment

The design of this course is guided by ÐÓ°ÉÂÛ̳ faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.