Data Science W207

Applied Machine Learning

3 units

Course Description

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.

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

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
Assistant Professor Alumni (PhD 2012)
207C South Hall

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Daniel Gillick
Assistant Adjunct Professor

Prerequisites

Data Science W201 and W203. Intermediate competency in Python, C, or Java, and competency in Linux, GitHub, and relevant Python libraries; or permission of instructor. Linear algebra is recommended. MIDS students only.

Last updated:

September 19, 2019