Math for Machine Learning

Learn fundamental concepts of artificial intelligence, data visualization, and how machines learn!

Math for Machine Learning on the campus of UC Berkeley

Program Highlights

Learn the basics of machine learning: What is data? What are models? How do machines learn?

Discover how math is used in machine learning: Math helps us design and train machine learning algorithms, evaluate their performance, and interpret their results.

Explore the different types of machine learning algorithms: How do linear regression, logistic regression, decision trees, and support vector machines work?

See how machine learning is used in the real world through the use of power image recognition, natural language processing, fraud detection, and many other applications.

**Some prior coding experience would be helpful.

Residential Tuition:
$5,798

Commuter Tuition:
$3,198

Session 1:
June 16, 2024 July 28, 2024

Session 3:
July 30, 2024 July 12, 2024

Session 5:
July 14, 2024 July 26, 2024

University of California at Berkeley
Berkeley, CA

Testimonials

"Summer Springboard expertly combines the worlds of academia and summer fun."

Taylor R.
"I felt so welcomed by my peers and staff at this program, and I am so excited for a real college experience!"

Carter G.
"SSB has been a completely new and valuable experience. It manages to encapsulate the feeling of a fun summer getaway while still providing valuable and enlightening experiences that educate and help to guide students through their future."

Emir B.

Course Overview

Students in this course will be introduced to the fundamental mathematical concepts that underpin machine learning. They will learn how to use math to design algorithms and select parameters, as well as the use of data analytics to analyze, visualize and interpret data.

Some prior coding experience would be helpful.

Meet your instructor

Instructor - Courses are taught by accomplished and passionate faculty recruited from many area colleges, universities and professional forums. Each faculty member is selected for their subject area expertise and proven ability to both challenge and captivate students.

Topics you'll explore

Hands on Learning

Students will spend time working on activities that explore vector and matrix operations, probability simulations, and work in groups on a linear regression project. They will also work on building a simple neural network to engage in deep learning. Throughout their time in this program, they will learn about career paths in machine learning and artificial intelligence and engage their soft skills in hands-on learning, group projects and interactive activities.

Course Structure

There are nine 3-hour class sessions over the two-week course. During week one, students have class from 9am-12pm, Monday - Friday. During week two, students have class from 9am-12pm Monday through Thursday. Wednesday afternoons are dedicated to additional academic time (excursions, speakers).

Career Exploration

Meet with experts in the artificial intelligence industry to learn more about majors and careers. Learn firsthand what it’s like to work in a world where machines can become reality. Excursions may include visits to AI labs and research facilities pioneering undiscovered innovations.