Overview

This course introduces machine learning concepts with a special focus on engineering applications. The course starts with a mathematical background required for machine learning and covers approaches for supervised learning (linear models, kernel methods, decision trees, neural networks) and unsupervised learning (clustering, dimensionality reduction), as well as theoretical foundations of machine learning (learning theory, optimization).

This course will help professionals to obtain the background necessary for excelling careers in machine learning, artificial intelligence, and related fields. We will cover basic concepts of probability prerequisite to understanding the machine learning tools. We will then introduce machine learning concepts such as supervised/unsupervised learning, model identification, clustering, hypothesis testing, and parameter estimation.

Sample instructor(s)

Duration

  • 3, 6, 9, or 12 hours

Customizable?

Yes, this course can be tailored to different audiences (with level of technicality varying for different audiences). The 3-hour version of the course will provide a high-level overview and focus on a subset of course topics, while the 12-hour version will cover all listed topics and include interactive activities for hands-on learning.

In-person or remote

  • Remote, in-person, or hybrid

Intended audience

This course is appropriate for professionals working in technology.

Takeaways

  • Background necessary for careers in machine learning, artificial intelligence, and related fields.
  • Apply machine learning techniques to solve engineering problems.

Course topics

  • Basic concepts of probability prerequisite to understanding machine learning tools.
  • More advanced topics including Markov chains, hypothesis testing, and maximum-likelihood estimation.
  • Introduction to machine learning concepts such as supervised/unsupervised learning, model identification, clustering, expectation maximization, etc.

Prerequisites

No specific programming languages are necessary, though a background in CS or IT is helpful is required for the more technical version of the course.

Materials

A copy of all presented information will be provided to participants.

Contact us

To learn about our custom programs and any upcoming open enrollments, reach out to Michael Lisanti.