The importance of data to business decisions, strategy and behavior has proven unparalleled in recent years. Predictive analytics, data mining and machine learning are tools giving us new methods for analyzing massive data sets. Companies place true value on individuals who understand and manipulate large data sets to provide informative outcomes.
Pivotal issues pertaining to mining massive data sets will range from how to deal with huge document databases and infinite streams of data to mining large social networks and web graphs.
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.
- Jure Leskovec Assistant Professor of Computer Science
- 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
3.0 - 4.0
Students enrolling under the non degree option are required to take the course for 4.0 units.
We highly recommend watching the course preview to ensure you have the requisite background and understand the scope of material covered.
Tuition & Fees
For course tuition, reduced tuition (SCPD member companies and United States Armed forces), and fees, please click Tuition & Fees.