EXECUTIVE EDUCATION

Machine Learning in Business

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Course Dates

STARTS ON

May 27, 2021

Course Duration

DURATION

2 months, online
4-6 hours per week

Course Duration

Bridge the Gap Between Business and Data Science Teams

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.

#1 in Canada for Open Enrolment

Source: The Financial Times 2020

What You'll Get

  • Training on how to communicate fluently on the topics of machine learning applications, processes, and strategies with data science teams, 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.

Program 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

Learn why there’s so much buzz about machine learning — what is the language of this domain, what are the three different types of ML, and examples of their applications for business.

Module 2:

Unsupervised Learning

Dive into the purpose of unsupervised learning models, and address managerial questions related to clustering and customer segmentation.

Module 3:

Regression Analysis and Its Extensions

Transition from statistics to machine learning with the use of regression models. Start to predict outcomes to inform business decisions.

Module 4:

Decision Trees

When facing a big decision, discover which questions on a decision tree are the highest priority

Module 5:

Support Vector Machines (SVMs)

Data points not cooperating for classification? Tap into a technique that helps to classify even your most stubborn data.

Module 6:

Neural Networks

Discover a fast-growth area of machine learning - neural networks - one of the most flexible approaches to supervised learning.

Module 7:

Reinforcement Learning

Imagine a computer playing chess against itself many times, using trial and error strategy to learn. This is reinforcement learning - good for defining the best sequence of decisions that allow you to solve a problem while maximizing a long-term reward.

Module 8:

Natural Language Processing (NLP)

How do your customers feel about your brand? Are financial analysts bullish about your business or not? One major application of NLP is sentiment analysis. But also voice assistants and chat bots.

Module 1:

Overview of Machine Learning and its Methodologies

Learn why there’s so much buzz about machine learning — what is the language of this domain, what are the three different types of ML, and examples of their applications for business.

Module 5:

Support Vector Machines (SVMs)

Data points not cooperating for classification? Tap into a technique that helps to classify even your most stubborn data.

Module 2:

Unsupervised Learning

Dive into the purpose of unsupervised learning models, and address managerial questions related to clustering and customer segmentation.

Module 6:

Neural Networks

Discover a fast-growth area of machine learning - neural networks - one of the most flexible approaches to supervised learning.

Module 3:

Regression Analysis and Its Extensions

Transition from statistics to machine learning with the use of regression models. Start to predict outcomes to inform business decisions.

Module 7:

Reinforcement Learning

Imagine a computer playing chess against itself many times, using trial and error strategy to learn. This is reinforcement learning - good for defining the best sequence of decisions that allow you to solve a problem while maximizing a long-term reward.

Module 4:

Decision Trees

When facing a big decision, discover which questions on a decision tree are the highest priority

Module 8:

Natural Language Processing (NLP)

How do your customers feel about your brand? Are financial analysts bullish about your business or not? One major application of NLP is sentiment analysis. But also voice assistants and chat bots.

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Program Experience

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Peer discussion groups

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Crowdsourcing activities

Decorative image relating to case studies and real data sets

Case studies and real data sets

Decorative image relating to simulation on sentient analysis

Simulation on sentiment analysis

Decorative image relating to optional coding exercises

Optional coding exercises

Decorative image relating to the ML Playbook

Your ML Playbook

Decorative image relating to real-world application exercises

Real-world application exercises

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Featured guest speakers

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

Program Faculty

John Hull

Professor of Finance, Maple Financial Group Chair in Derivatives and Risk Management, Academic Director, Rotman Financial Innovation Hub

John Hull is the Maple Financial Professor of Derivatives and Risk Management and Academic Director, Rotman Financial Innovation Hub at Rotman. His research has an applied focus and is concerned with risk management, bank regulation, valuation of derivatives, and machine learning. He is best known for his books Risk Management and Financial Institutions (now in its 5th. edition), Options, Futures, and Other Derivatives (now in its 10th edition), and Fundamentals of Futures and Options Markets (now in its 9th edition). His books have been translated into many languages and are widely used in trading rooms throughout the world, as well as in the classroom. John is also co-director of the Rotman's Master of Financial Risk Management & Master of Finance Programs.

Dr. Hull holds a Ph.D. in Finance from Cranfield University; an M.A. in Operational Research from Lancaster University; an M.A. and B.A. in Mathematics from University of Cambridge.

Certificate

Certificate

Upon successful completion of the program, you’ll earn a digital certificate of completion from the Rotman School of Management. This program counts toward a Rotman Excellence in Executive Leadership Certificate.

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Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the Rotman School of Management.

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