About the course
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.
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. ...
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