Applied Machine Learning

FOUNDATION COURSE
3 units

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

DESIGNED BY
Josh Blumenstock and Dan Gillick

Machine learning is a rapidly growing field at the intersection of computer science and statistics that is concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. The goal of this course is to provide 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. Through a variety of lecture examples and programming projects, students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

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