Practical hands-on experience will entail the design of algorithms for analyzing very large amounts of data and to learn existing data mining and machine learning algorithms. As a useful analytic tool, case studies will provide first-hand insight into how big data problems and their solutions allow companies like Google to succeed in the market.
- Shingling, minhashing, random hyperplanes, locality-sensitive hashing
- Dimensionality reduction: UV, SVD, and CUR decompositions
- Algorithms for very large scale mining: clustering, nearest-neighbor search, gradient descent, support-vector machines, classification, and regression
- Submodular function optimization
ResourcesPlease view the course website for additional information.
This course is offered for 3-4 units. Students enrolling under the non degree option are required to take the course for 4 units.
ScheduleThis course is typically offered in the Winter quarter.