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TECH 105 — Statistics for AI, Machine Learning, and Data Science

Quarter: Spring
Day(s): Wednesdays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Apr 3—Jun 5
Time: 5:30—7:20 pm (PT)
Refund Deadline: Apr 5
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)
Apr 3—Jun 5
10 weeks
Refund Date
Apr 5
2 Units
Grade Restriction
No letter grade
Gregory Ryslik, Patrick Staples
ACCESS THE SYLLABUS » (subject to change)
There have been tremendous advancements in artificial intelligence and machine learning in recent years across a variety of fields ranging from autonomous driving to disease prediction and natural language processing. All of these advancements, however, are deeply rooted in the fields of statistics and computer science. This course will give students a high-level overview of some of the most common concepts in statistics that make AI and ML possible. Indeed, many of the newest algorithms, such as neural networks, random forests, and k-nearest neighbors, use statistics not only to build a model but also to evaluate its accuracy. The course will cover two broad areas of statistics: inference and prediction. The inference portion will introduce common statistical concepts that allow us to understand a population and test hypotheses (such as performing A/B tests and calculating and interpreting p-values). The prediction section will begin with the simplest of algorithms (linear regression) and gradually touch upon more advanced topics, such as random forests and cross-validation. Real-world examples from the fields of healthcare, genetics, marketing, and manufacturing will be used. By the end of the course, students will have a high-level understanding of common statistical tools used in AI and ML algorithms and will be able to derive their own conclusions from 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.

CTO, 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.