Data Mining Course | University of Washington

Comprehensive data mining course at the University of Washington, covering a wide range of techniques and algorithms, taught by an expert in the field.

Introduction

CSEP 546 - Data Mining is a course offered at the University of Washington in Spring 2016. The course covers various topics in data mining, including inductive learning, decision trees, rule induction, instance-based learning, Bayesian learning, neural networks, model ensembles, learning theory, support vector machines, clustering, and dimensionality reduction.

Highlights

• Covers a wide range of data mining techniques and algorithms
• Taught by Professor Pedro Domingos, an expert in the field of data mining
• Includes hands-on assignments using real-world datasets
• Provides access to video archives and live-streamed lectures

Recommendation

This course is recommended for students interested in data mining, machine learning, and their applications. It provides a comprehensive overview of the field and hands-on experience with various data mining techniques. The course is suitable for both beginners and experienced learners in the field of data mining.