Machine Learning

Rotman School of Management

Rotman School of Management

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Who should attend

This program is designed for anyone who wants to understand how machine learning techniques can help them leverage their data to achieve better outcomes. It is particularly applicable for:

  • C-Suite executives to help guide investments in machine learning and data science resources

  • Functional leaders such as directors of marketing, technology, innovation, or strategy to understand the business value drivers embedded in machine learning and to lead digital transformation projects with ‘inside knowledge

  • Technologists such as IT and solutions architects to create enterprise IT architecture using leading ML technology frameworks

  • Product and project managers who strive to enhance the user experience/engagement of products and to prioritize product features through enhanced understanding of their customer’s needs and behaviours

  • Mid-level managers who strive to enable data-driven management techniques and to automate repetitive tasks for more efficient management

  • Consultants who strive to help their clients develop strategies around ML strategy, capabilities, and talent

About the course

Machine Learning, a sub-domain of Artificial Intelligence, employs techniques used for analyzing large data sets and making predictions. These techniques are being adopted by organizations across all industries and all functional areas to achieve better outcomes – whether in healthcare for better patient outcomes or in financial services to stave off bad risk.

In order to reach optimal performance, you need to adopt the mindset and language of data scientists. In Machine Learning in Business, Rotman delivers just that - the bridge for business professionals to communicate effectively with analysts and data scientists to drive better business outcomes.

What you’ll get:

  • Training on how to communicate fluently on the topics of machine learning applications, processes, and strategies with data science team either internal or external.
  • Expert guidance on how to describe common algorithms and appropriate business applications for each.
  • Techniques to identify ways in which machine learning can support business leadership to improve understanding of customers and use data to make predictions.
  • A deep understanding of the approaches used by data science teams to work with them and achieve better outcomes.

Curriculum

New content is released each week, such that the cohort moves in unison through the program with group discussions enriching the learning experience. Because the lectures are recorded, there is flexibility for you to learn on your schedule. Live program support is available throughout the entire learning journey .

  • Module 1: Overview of Machine Learning and its Methodologies
  • Module 2: Unsupervised Learning
  • Module 3: Regression Analysis and Its Extensions
  • Module 4: Decision Trees
  • Module 5: Support Vector Machines (SVMs)
  • Module 6: Neural Networks
  • Module 7: Reinforcement Learning Module 8: Natural Language Processing (NLP)

Experts

John Hull

Maple Financial Group Chair in Derivatives and Risk Management Professor of Finance, Co-Director of the Rotman Master of Finance program Degrees:PhD, Cranfield University MA, Lancaster University MA, University of Cambridge BA, University of Cambridge

Videos and materials

Machine Learning at Rotman School of Management

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.

We are happy to help you find a suitable online alternative.