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STAT 05 — Statistics for AI, Machine Learning, and Data Science

Quarter: Winter
Day(s): Wednesdays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Jan 17—Mar 20
Time: 5:30—7:20 pm (PT)
Refund Deadline: Jan 19
Units: 2
Grade Restriction: No letter grade
Tuition: $595
Instructor(s): Gregory Ryslik, Patrick Staples
Class Recording Available: Yes
Status: Open
ACCESS THE SYLLABUS » (subject to change)
Live Online(About Formats)
5:30—7:20 pm (PT)
Jan 17—Mar 20
10 weeks
Refund Date
Jan 19
2 Units
Grade Restriction
No letter grade
Gregory Ryslik, Patrick Staples
ACCESS THE SYLLABUS » (subject to change)
The machine learning that powers the models used in everything from autonomous vehicles to disease prediction hinges on understanding statistics and computer science. Those without at least a basic understanding of these fields can’t interpret a model’s output or make informed decisions about how to use it effectively. This course will give students a high-level understanding of some of the most common concepts in statistics that make AI and ML possible, including probability distributions, Bayes’s theorem, and entropy and information gain.

Designed for those in technology or technology-adjacent roles, the course is split into two main sections. In the first section, students will explore foundational statistical concepts related to population and hypothesis testing, like A/B testing and p-value interpretation. The second section will cover topics ranging from linear regression to tree-based algorithms and cross-validation. These principles are explained using real-world examples from healthcare to marketing, ensuring contextual understanding. By the end of the course, students will have an understanding of standard statistical tools used in AI and ML algorithms and will be able to derive solid conclusions from ML models based on statistical studies.

Knowledge of mathematics and statistics is recommended. Such foundational knowledge ensures students can grasp concepts and theories effectively. Enrolling in the course without prior education in these topics may lead to challenges in understanding and application of course content.

Chief Technology Officer, Compass Pathways

Gregory Ryslik has held senior positions in the biotech, actuarial science, and automotive industries at companies including Tesla, Genentech, PricewaterhouseCoopers, and several startups. He received a PhD in biostatistics from Yale.

Biostatistician; Data Scientist

Patrick Staples has experience in the healthtech, fintech, and pharmaceutical industries. He received a PhD in biostatistics from Harvard.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.