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a965da1 · Feb 5, 2016

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Week3

#Week 3 Overview#

Introduction to Supervised Learning

In this week, you will learn about several basic supervised learning algorithms, including the k-nearest neighbor algorithm, support vector machine, and naive Bayes. Each of these algorithms have Python implementations in the scikit learn library and are easy to apply to a wide range of classification or regression problems. In addition, you will learn about classification and regression metrics, such as the precision, recall, and f1 score, and how to use them effectively to compare the results of different machine learning algorithms.

Objectives

By the end of this lesson, you should be able to:######
  • Know the basic classification and regression metrics.
  • Understand the k-nearest neighbor algorithm
  • Understand the support vector machine algorithm
  • Understand the naive Bayes algorithm

Activities and Assignments

Activities and Assignments Time Estimate Deadline* Points
Week 3 Introduction Video 10 Minutes Tuesday 20
Week 3 Lesson 1: Supervised Learning: k-Nearest Neighbor 2 Hours Thursday 20
Week 3 Lesson 2: Supervised Learning: Support Vector Machine 2 Hours Thursday 20
Week 3 Lesson 3: Supervised Learning: Naive Bayes 2 Hours Thursday 20
Week 3 Quiz 45 Minutes Friday 70
Week 3 Assignment Submission 4 Hours The following Monday 80 Instructor, 40 Peer
Week 3 Completion of Peer Review 2 Hours The following Saturday 30

Please note that unless otherwise noted, the due time is 6pm Central time!