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Course Description

The goal of this course is to give a broad description of mathematical and statistical underpinnings of Machine Learning (ML) tools and their Artificial Intelligence (AI) applications. AI is now ubiquitous in every industry, and scientific research is not an exception. While AI applications are quite complex and achieve mind-boggling feats, there is some misunderstanding and mysticism around what they can do and how they achieve it. Often, a more accurate term for AI would be ML, and deep inside these cutting-edge tools boil down to time-tested Statistical Modeling (SM) methods that are at least half a century old; therefore, scientists with some statistical background are very well positioned to understand and apply these techniques in their research and beyond. In this course we will explore the fundamentals of SM/ML/AI using a conceptual and intuitive that will equip scientists of a wide range of quantitative backgrounds to master these tools.

Learner Outcomes

When you complete the course successfully, you will be able to:

  • State the essential mathematical and statistical concepts underpinning Machine Learning (ML) approaches: for any statistical model, including AI/ML you will be able to identify what precise mathematical operations are being employed, what were the options, and which choices were made 
  • Identify equivalences and contrasts between Statistical Modeling (SM) methods, Machine Learning approaches, and Artificial Intelligence applications: you will be able to relate in formal statistical terms what statistical principles underlie AI/ML algorithms, as well as the differences from SM in their application and practice 
  • Apply, assess, and criticize methods and applications of Statistical Modeling and Machine Learning: you will be able to both conceptually and (to some extent) technically identify the advantages, pitfalls, and trade-offs of modeling and implementation choices 

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.


 

Textbook Information

A textbook is available for this course.

Click here to view a textbook list for FAES courses and purchasing information. Please note that tuition does not include textbooks.

Prerequisites

This course has no prerequisites, but some background in math (calculus and linear algebra) and statistics will be helpful.

Refund

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Scholarship and Funding

Are you a self-funded student? FAES offers scholarship options. Click here for more information and to apply. 

Looking for resources to help you acquire funding for your continued education? Click here for our funding justification guide. 

Photo Release

By registering for this event, you agree to allow FAES to take photographs of you during the event and to use these photos for promotional purposes, including on our website, social media, and marketing materials, without further compensation. You understand that you have no right to review or approve the final use of these images.

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Section Title
Fundamentals of Quantitative and Statistical Thinking for Machine Learning and Artificial Intelligence
Type
Online Asynchronous
Dates
Jan 28, 2026 to Mar 17, 2026
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
Instructor(s)
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