Applied Machine Learning

Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.

Skill Sets

Experimental design / Working with machine learning algorithms / Feature engineering / Prediction vs. explanation / Network analysis / Collaborative filtering

Tools

Python / Python libraries for linear algebra, plotting, machine learning: NumPy, Matplotlib, sk-learn / GitHub for submitting project code

Course Designers

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Joshua Blumenstock

Associate Professor Alumni (PhD 2012)

josh@ischool.berkeley.edu

207C South Hall

Website

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Daniel Gillick

Assistant Adjunct Professor

dgillick@ischool.berkeley.edu

Previously listed as DATASCI W207.

Course must be taken for a letter grade to fulfill degree requirements.