Introduction to Machine Learning

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Course Details

About this Course


Discuss fundamental concepts of statistical analyses, mathematical modeling and optimization techniques, and learn how they relate to a set of fundamental algorithms and concepts as the foundation for machine learning. Explore key foundational concepts such as probability and basic statistical methods to gradually develop the skills for modelling, designing and evaluating supervised and unsupervised learning models for a variety of real world tasks, such as forecasting, prediction and outlier detection.

Topics include:

  • Basics of probability and statistics
  • Attribute types, distance measures and tools
  • Introduction to optimization
  • Discriminative models: supervised learning
  • Generative models
  • Model evaluation and parameter tuning
  • Ensemble methods

Program Overview

Complete the courses listed below to earn the certificate. You may be able to take individual courses without enrolling in the certificate program; check the course pages for details.