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STAT 05 W — Statistics for Artificial Intelligence, Machine Learning, and Data Science: An Introduction

Quarter: Winter
Course Format: Online (System Requirements)
Duration: 9 weeks
Date(s): Jan 21—Mar 20
Drop Deadline: Jan 24
Unit: 1
Grade Restriction: No letter grade
Tuition: $585
Instructor(s): Gregory Ryslik
Limit: 65
Status: Closed
Please Note: The full tuition refund deadline for this course is January 24th at 5:00 pm (PT); 50% tuition refund deadline is January 29th at 5:00 pm (PT)
Winter
Date(s)
Jan 21—Mar 20
9 weeks
Drop By
Jan 24
1 Unit
Fees
$585
Grade Restriction
No letter grade
Instructor(s):
Gregory Ryslik
Limit
65
Closed
Please Note: The full tuition refund deadline for this course is January 24th at 5:00 pm (PT); 50% tuition refund deadline is January 29th at 5:00 pm (PT)
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, to natural language processing. All of these advancements, however, are deeply rooted in the fields of statistics and computer science. This online 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 will be used from the fields of healthcare, genetics, marketing, and manufacturing. By the end of the course, students will have a high-level understanding of common statistical tools used in AI and ML algorithms and be able to derive their own conclusions from statistical studies.

WHAT MAKES OUR ONLINE COURSES UNIQUE:

  • Course sizes are limited.
    You won't have 5,000 classmates. This course's enrollment is capped at 65 participants.

  • Frequent interaction with the instructor.
    You aren't expected to work through the material alone. Instructors will answer questions and interact with students on the discussion board and through weekly video meetings.

  • Study with a vibrant peer group.
    Stanford Continuing Studies courses attract thoughtful and engaged students who take courses for the love of learning. Students in each course will exchange ideas with one another through easy-to-use message boards as well as optional weekly real-time video conferences.

  • Direct feedback from the instructor.
    Instructors will review and offer feedback on assignment submissions. Students are not required to turn in assignments, but for those who do, their work is graded by the instructor.

  • Courses offer the flexibility to participate on your own schedule.
    Course work is completed on a weekly basis when you have the time. You can log in and participate in the class whenever it's convenient for you. If you can’t attend the weekly video meetings, the sessions are always recorded for you and your instructor is just an email away.

  • This course is offered through Stanford Continuing Studies.
    To learn more about the program, visit our About Us page. For more information on the online format, please visit the FAQ page.

Weekly course lecturers will be conducted via live videoconferencing sessions. The first session will take place on Tuesday, January 21 from 6:00 - 7:30 pm PT. The remaining class meetings will be held on Monday evenings from 6:00 - 7:30 pm PT. The duration will be approximately 90 minutes, but the lectures could run slightly shorter or longer depending on student questions. Most of the course material will be covered during the live sessions, and although the lectures will be recorded, student attendance is recommended.

This course has no specific prerequisites and can be taken on a variety of levels. Beginners are encouraged to listen to the lectures and learn basic concepts. By the end of the course, beginners should have a sense of what these algorithms do. On a higher level, intermediate students can work some of the introductory problems that will be provided. On an advanced level (for students with a substantial math background who are interested in becoming data scientists), difficult math and stats problems will be covered. Students should be aware that this course will not make them an AI expert (this is not possible in nine sessions). On a basic level, the course will give students a taste of what statistics for AI is all about. On the highest level, the course will give students a strong sense of what they need (and should be excited about) in order to pursue a career in this area.

Gregory Ryslik, Chief Data Officer, Celsius Therapeutics; Adjunct Assistant Professor of Statistics, Pennsylvania State

Gregory Ryslik is a statistician who has worked in the biotech, actuarial science, and automotive industries, including the data science team for service at Tesla and nonclinical machine learning at Genentech. He received an MA in statistics from Columbia and a PhD in biostatistics from Yale.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)