What Education Programs Does Machine Learning at Berkeley Run?

Machine Learning at Berkeley (ML@B) runs three education offerings described on its site: a 2-unit DeCal course, high school workshops, and a Udacity Nanodegree Program. The organization frames all three around one goal — making machine learning more accessible at every level, from high school students to Berkeley undergraduates to independent online learners. If you are a current or prospective Berkeley student, the DeCal is the most direct entry point; if you are outside UC Berkeley, the workshops and Nanodegree are the relevant channels.

The Three Education Programs at a Glance

The 2-Unit DeCal Course

DeCal (Democratic Education at Cal) courses are student-taught, for-credit courses at UC Berkeley. ML@B lists its DeCal as a 2-unit offering, which means it carries a lighter credit load than a standard technical course and is designed to sit alongside a normal semester schedule. The site does not publish the syllabus, meeting times, or enrollment caps, so the practical step is to check the current semester's DeCal listing through Berkeley's course catalog and confirm the facilitator and enrollment process there.

High School Workshops

ML@B describes running workshops for high school students as part of its education mission. The site does not specify grade levels, locations, scheduling, or whether workshops run through school partnerships or direct sign-ups. If you are a teacher or program coordinator interested in hosting one, the site's contact channel is the place to ask about current availability rather than assuming a recurring schedule.

The Udacity Nanodegree Program

ML@B names a Udacity Nanodegree Program among its education offerings, positioning it as a way to reach learners beyond the Berkeley campus. The site does not state which nanodegree track, whether ML@B members contribute content or act as mentors, or what the cost and time commitment are. Those details live on Udacity's side, so treat the ML@B mention as a pointer to the partnership rather than a full program description.

How the Education Work Connects to the Rest of ML@B

Education is one of three pillars on the site, alongside consulting and research. That matters for how you read the education programs:

• Consulting partnerships connect teams of experienced ML@B members to companies with ML problems.
• Research gives students and researchers a platform to collaborate, with compute resources and funding to attend AI/ML conferences.
• Education is the outreach arm — the DeCal, workshops, and Nanodegree are how members pass knowledge outward.

In practice, the education programs are run by the same student community that does the consulting and research work, which is why the site emphasizes accessibility across levels rather than a single flagship course.

Who Should Use Which Program

• Berkeley undergraduates who want structured, low-commitment exposure: start with the 2-unit DeCal and confirm the current semester's listing.
• High school students or teachers looking for an introduction to ML: ask about workshops through the contact channel, since scheduling is not published.
• Learners anywhere who want an online credential: look up the Udacity Nanodegree directly for curriculum, price, and timeline.
• Companies or researchers who want deeper involvement: the consulting and research partnership pages are the better fit than the education programs.

Practical Next Steps and Common Gaps

The site states that ML@B accepts applications at the start of every Fall and Spring semester, so timing your interest around those windows matters if you want to join the organization itself rather than just take a course. Two things to verify before committing: whether the DeCal is offered in your specific semester (student-run courses can lapse), and what the Nanodegree actually covers and costs, since ML@B's page only names it. Neither the site nor its pricing signals indicate that any of these programs are free, so confirm cost and eligibility through the DeCal listing and Udacity respectively.