Cs 4407 Data Mining And Machine Learning

CS 4407: Data Mining and Machine Learning

Prerequisites:

CS3304: Analysis of Algorithms. Recommended – CS4402: Comparative Programming Languages.

Course Description:

This course will investigate data mining and machine learning algorithms in both supervised and unsupervised learning. Students will understand how to use the R programming language for performing clustering, classification, and regression analysis. Students will learn the capabilities and operation of many algorithms including decision trees, k-means, k-nearest neighbors, linear regression, ID3 for Decision Trees, and the Perceptron.

Required Textbook and Materials:

The main required textbooks for this course are listed below and can be readily accessed using the provided links. There may be additional required/recommended readings, supplemental materials, or other resources and websites necessary for lessons; these will be provided for you in the course’s General Information and Forums area, and throughout the term via the weekly course Unit areas and the Learning Guides.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York, NY: Springer. Available for download here.
Software Requirements/Installation: This course will make use of two different software tools. The first is the R programming language environment and the second is the basic prop neural network simulator. Both of these tools will be available in the Virtual Computing Lab or you can install them on your own computer.

Learning Objectives and Outcomes:

By the end of this course students will be able to:

1. Explain the differences among the three main styles of learning: supervised, reinforcement, and unsupervised.
2. Implement simple supervised learning, reinforcement learning, and unsupervised learning examples using R.
3. Understand a range of machine learning algorithms along with their strengths and weaknesses.
4. Understand the basic operation of machine learning algorithms including decision trees, neural networks, K nearest neighbors, K means clustering, and regression.
5. Be able to apply machine learning algorithms to solve simple problems.

Course Schedule and Topics:

This course will cover the following topics in eight learning sessions, with one Unit per week. The Final Exam will take place during Week/Unit 9 (CIU time).

Week 1: Unit 1 – Introduction to Data Mining and Machine Learning

Week 2: Unit 2 – Tools and Technologies for Data Mining and Machine Learning

Week 3: Unit 3 – Regression

Week 4: Unit 4 – Classification

Week 5: Unit 5 – Decision Trees

Week 6: Unit 6 – Artificial Neural Networks – Part 1

Week 7: Unit 7 – Artificial Neural Networks – Part 2

Week 8: Unit 8 – Unsupervised Learning – Clustering

Week 9: Unit 9 – Course Review and Final Exam