#Week 3 Overview#
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.
- 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 | 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!