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Course Description

Save when you register for both MATH 410 and STAT 504 as a bundle!

This bundle provides a comprehensive foundation in the mathematical and statistical principles that underpin modern artificial intelligence, machine learning, and data science. Students develop both the computational skills and conceptual understanding needed to analyze data, build predictive models, and critically evaluate AI systems. Topics include calculus, probability, statistics, linear algebra, regression, classification, optimization, and the mathematical foundations of neural networks. Throughout the course, students connect classical statistical modeling with modern machine learning methods, gaining an understanding of the shared principles, key differences, and trade-offs among statistical, machine learning, and AI approaches. Using Python-based tools, students apply quantitative methods to real-world problems while developing the ability to interpret, assess, and communicate the mathematical reasoning behind AI and machine learning models.

Learner Outcomes

When you complete these courses successfully, you will be able to:
  • Apply foundational mathematical concepts, including functions, logarithms, derivatives, integrals, linear algebra, and optimization, to problems in artificial intelligence, machine learning, and data science
  • Apply probability theory, probability distributions, and statistical methods to model uncertainty, analyze data, and support quantitative decision-making
  • Summarize, visualize, and interpret data using descriptive statistics, inferential statistics, hypothesis testing, confidence intervals, and significance testing
  • Develop, evaluate, and interpret regression and classification models using appropriate mathematical, statistical, and computational techniques
  • Explain the mathematical foundations of neural networks, including weights, biases, activation functions, forward propagation, backpropagation, gradient descent, and optimization
  • Describe the mathematical and statistical principles that underpin machine learning and artificial intelligence algorithms and explain how these methods relate to traditional statistical modeling
  • Compare statistical modeling, machine learning, and artificial intelligence approaches, identifying their strengths, limitations, assumptions, and appropriate applications
  • Critically assess modeling choices, evaluate model performance, and communicate quantitative findings effectively using mathematical, statistical, and computational reasoning

Microcredential(s)

These courses apply toward the Bioinformatics Endeavor and Statistics and Mathematics for Biomedical Researchers: Bioinformatics Specialization digital badges.

     

Textbook Information

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

Prerequisites

Previous academic experience with advanced math areas such as linear algebra, multivariable calculus, and statistics is strongly recommended. 

Refund
Follow the link to review FAES Tuition Refund Policy.

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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To Register Click on "Add to Cart"

Section Title
2-COURSE BUNDLE - Essential Mathematics for AI and Statistical Thinking for AI and Machine Learning
Type
Online Asynchronous
Dates
Aug 26, 2026 to Dec 08, 2026
Total Cost (Includes $100 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (4 Credits) $1,460.00
Potential Discount(s)
Available for Academic Credit
4 Credit(s)
Required fields are indicated by .