CS 445 Machine Learning
Fall 2020
Week 4 Study Guide
Learning Objectives
- Define and compute (by hand and in code) Euclidean and Manhatten distances
- Define the K-Nearest Neighbors classifier (with pseudo-code).
- Define normalization and characterize the impact this has on classifiers that utilize distances
- Define the curse of dimensionality and characterize the impact this has on classifiers that employ a large number of features and utilize distances
- Define feature selection and the forward selection and backwards selection greedy approaches to this address the problem of feature selection.
Resources
- Data Mining Textbook: 3.3 - 3.8
- Lecture Slides
Labs
Deliverables
| Topic | Description |
| Reading Quiz | Complete the reading quiz on Canvas by the due date (indicated in Canvas) |
| PA 1 | Submit code to Autolab and documents are due in Canvas (see the due date in Canvas). |