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STAT 05 — Statistics for Artificial Intelligence, Machine Learning, and Data Science

Quarter: Winter
Day(s): Thursdays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Jan 12—Mar 16
Time: 5:30—7:20 pm (PT)
Refund Deadline: Jan 14
Units: 2
Grade Restriction: No letter grade
Tuition: $565
Instructor(s): Gregory Ryslik, Patrick Staples
Class Recording Available: Yes
Status: Open
Please Note: This course has a different schedule than what appears in the print catalog. The course will take place over 10 Thursdays, January 12 - March 16, 5:30 - 7:20 pm (PT).
DOWNLOAD THE SYLLABUS » (subject to change)
Winter
Live Online(About Formats)
Thursdays
5:30—7:20 pm (PT)
Date(s)
Jan 12—Mar 16
10 weeks
Refund Date
Jan 14
2 Units
Fees
$565
Grade Restriction
No letter grade
Instructor(s):
Gregory Ryslik, Patrick Staples
Recording
Yes
Open
Please Note: This course has a different schedule than what appears in the print catalog. The course will take place over 10 Thursdays, January 12 - March 16, 5:30 - 7:20 pm (PT).
DOWNLOAD THE SYLLABUS » (subject to change)
There have been tremendous advancements in artificial intelligence (AI) and machine learning (ML) 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.

GREGORY RYSLIK
Executive Vice President of AI, Engineering, Digital Health Research & Technology, 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.

PATRICK STAPLES
Biostatistician and Data Scientist

Patrick Staples is a biostatistician and data scientist, with 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.