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Live Virtual
Course Length
2 Days
Course Fee
1.2 CEUs
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This course may be taken individually or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence.

Machine learning is a rapidly expanding area with a diverse collection of tools and approaches. Successfully applying such methods to real tasks may seem to require expertise that many do not possess. However, all these methods share the same basic concepts, use the same building blocks.

Understanding these basics, formulations, and when they are appropriate, is key to using machine learning techniques successfully in practice. This foundational course covers the essential concepts and methods in machine learning, providing participants with an entry level expertise they need to get started and quickly move ahead.

Participant Takeaways

  • Understand the basic machine learning concepts and methods including neural networks
  • Learn how to formulate/set up problems as machine learning tasks
  • Assess which types of methods are likely to be useful for a given class of problems
  • Understand strengths and weakness of learning algorithms

Who Should Attend

This course is appropriate to obtain a better understanding of machine learning basics. It is most suitable for those with an undergraduate degree in computer science or other related technical areas. A high-level understanding of programming (thinking in terms of programs) is helpful.

The foundational course describes key concepts, formulations, algorithms, and practical knowledge for people who are getting started or need to brush up in machine learning, and provides participants with core knowledge to succeed in the advanced level course. 


Laptops are required for this course. Tablets will not be sufficient for the computing activities performed in this course.

Program Outline

Class runs 10:00 am - 3:45pm on Monday and 9:00am - 3:30pm on Tuesday.

Mon: (5.5h)
[10:00am] introduction to ML (1h)
[11:00am] formulation of ML problems (1h)
[12:00pm] lunch break
[ 1:00pm] linear classification/regression (1h)
[ 2:00pm] coffee break (1h)
[ 2:15pm] loss, regularization, gradient algorithms (1.5h)
[ 3:45pm] tutorial on using ML packages (1h)
Tue: (6h)

[ 9:00am] features, missing data (1h)
[10:00am] non-linear classification (1h)
[11:00am] coffee break
[11:15am] feed-forward neural networks: representation (1h)
[12:15pm] lunch break (1h)
[ 1:15pm] neural networks: algorithms (1h)
[ 2:15pm] coffee break
[ 2:30pm] convolutional networks (images) (1h)
[ 3:30pm] tutorial on using DNN packages (1h)

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