Course Preview : Machine Learning in Business at Rotman School

23 November 2022 | 0 min read

[Video Transcript] Machine Learning in Business at Rotman School

Welcome to the course. I hope you're ready to get started. Before we do, I'd like to run you through what you can expect from this course over the next several weeks. First of all, let's talk about structure. We've built this course to run over eight weeks. Each week starts with a pre-reading, of a chapter from my book. This will help you get an overview of the topic we'll be covering. Most chapters include some math to demonstrate the algorithms discussed. If the math is a bit beyond you, don't worry about it because you'll still be able to understand the concepts. If you're a quant who likes math, feel free to dive more deeply into it. Once you've gone through the pre-readings, we'll launch into the recorded videos. In between the lectures, you'll find activities to practice with the key concepts and reinforce your learning. We won't teach you to program, but if you have some programming skills, you can take a look at the source code and play around with it to view different results.

Ready to Learn More? Apply to Enroll in: Machine Learning in Business

At the end of each week, you'll reflect on your learning and plot. Next steps to apply what you've learned in your professional practice. This is an important part of the course that we hope you'll use as a roadmap to better manage your team's data science projects. Now let's talk about what this course is all about. The aim of the course is to equip executives with the knowledge that will enable them to work productively with data scientists. The course is not a superficial overview. It takes a deep dive into the main algorithms used by data scientists. Participants will learn how machine learning can enable them to understand their customers better, automate routine decisions, and use data to make predictions. A distinctive feature of the course is that it explains key concepts without using vector or matrix algebra and without using calculus. Software skills are not assumed.

Participants who have Python skills or are developing them will have access to code, and they can use that for the example decisions that are considered. To get participants up to speed in machine learning, I've organized the course into eight key topics. In the first module, we provide an overview of the course defining machine learning, exploring different types and applications, and getting acquainted with key terms. In the second module, we discuss unsupervised learning algorithms in their applications. Then we launch into supervised learning algorithms for prediction, regression, decision trees, support vector machines, and so on. In the last few modules, we'll explore algorithms that enter into the realm of artificial intelligence, neural networks, reinforcement learning, and natural language processing.

As you move through the course, a few things to keep in mind. Yours is a global cohort from a wide variety of cultural and professional backgrounds. I hope you'll use the program as an opportunity to compare notes and networks. The program includes 24-hour support for technical issues through program support. Learning facilitators with experience in data science will respond to content inquiries and host office hours to further their learning. Do take advantage of these. I hope you're ready for an exciting eight weeks of learning. Let's get started.

[End of Video Transcript]

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