Machine Learning at Scale
University of Edinburgh ยท Scotland ยท EPCC11013
Machine Learning at Scale is a subject at University of Edinburgh, Scotland. It covers topics like data-mining, deep-learning, machine-learning, statistical-learning. It is part of 2 degrees. The information below is compiled from public course material โ approximate, not official.
University of Edinburgh
EPCC11013
3.00
Topics covered
Degrees that include Machine Learning at Scale
- High Performance Computing (MSc) (Full-time)
- High Performance Computing with Data Science (MSc) (Full-time)
Frequently asked questions
What is Machine Learning at Scale about?
Machine Learning at Scale covers topics like data-mining, deep-learning, machine-learning, statistical-learning.
Which degrees include Machine Learning at Scale?
Machine Learning at Scale is part of 2 degrees, including High Performance Computing (MSc) (Full-time), High Performance Computing with Data Science (MSc) (Full-time).
How many credit points is Machine Learning at Scale?
Machine Learning at Scale at University of Edinburgh is typically around 3.00 credit points. Approximate โ confirm with the university.
Where is Machine Learning at Scale taught?
Machine Learning at Scale is taught at University of Edinburgh, Scotland.
Studying Machine Learning at Scale?
Peernovo helps students research, choose and thrive at university โ across Australia, the US, the UK and Canada โ powered by our AI backbone:
- Explore & compare degrees โ across universities and countries with honest, specific information.
- "What Can I Be?" career guidance โ an AI pathway councillor that maps degrees to careers.
- See who's studying โ your degree at your university โ find classmates and study groups.
- AI Task Coach โ a Socratic study companion that plans your work around your life.
- Honours & Thesis Research Coach โ structure and move your research project forward.
- Smart study planner, exam prep and a daily digest โ to keep you on track.
Sources: University of Edinburgh public course catalogue. Information is compiled and summarised โ not copied verbatim โ and is approximate; always confirm with the original source.
Something not right on this page, or want it changed? Let us know and we'll review it.
