About
The Graduate Division serves more than 13,000 students in over 100 graduate degree programs. We are here to help you from the time you are admitted until you complete your graduate program.
Admissions
We're thrilled you're considering Berkeley for your graduate study. We offer more than 100 programs for master's, professional, and doctoral students to pursue their dreams.
Financial Support
Working toward obtaining your graduate degree at Berkeley is an exciting and challenging endeavor, but funding your graduate education shouldn’t be your greatest challenge.
Academics
We're here to support you as you progress through your degree, and help you understand the policies and procedures that inform your graduate education.
Professional Development
GradPro, the Graduate Writing Center, and the GSI Teaching & Resource Center can support you in your academic and professional development at all stages of your degree program and in preparing for your career.
Diversity
The Office for Graduate Diversity provides support and services for prospective and continuing students in an effort to support and sustain a more diverse graduate student community.
Giving
Your gift allows us to deliver an inclusive, world-class experience to graduate students, so they can make a difference at Berkeley and beyond.
Back to All Events
This two-part workshop provides an introduction to machine learning algorithms using the tidymodels package. It covers what machine learning is, which problems it is most and least equipped to address, and explores the tidymodels framework to fit supervised machine learning models in R.
tidymodels
Addressing machine learning problems requires a deep conceptual understanding of the material. While the workshop will cover coding in R, it will also dedicate a significant portion of the time to motivating machine learning techniques.
By the end of the workshop, learners should feel prepared to explore machine learning approaches for their own data problems. This workshop does not cover unsupervised machine learning techniques.
Prerequisites: Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.