Scalable Machine Learning | UC Berkeley | Alex Smola

Comprehensive course on scalable machine learning techniques for large-scale data analysis and internet applications, covering systems, statistics, algorithms, and more.

Introduction

Scalable Machine Learning occurs when Statistics, Systems, Machine Learning and Data Mining are combined into flexible, often nonparametric, and scalable techniques for analyzing large amounts of data at internet scale. This class aims to teach methods which are going to power the next generation of internet applications.

Highlights

• Covers systems and processing paradigms, statistical analysis, algorithms for data streams, generalized linear methods, large scale convex optimization, kernels, graphical models and inference algorithms
• Explores applications including social recommender systems, real time analytics, spam filtering, topic models, and document analysis

Recommendation

This course is recommended for students interested in learning scalable machine learning techniques that can be applied to large-scale data analysis and internet applications. It provides a comprehensive overview of the key concepts and methods in this rapidly evolving field.