CMSY 232 Machine Learning II

Machine learning involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. It is a computer algorithm that uses statistical techniques to understand patterns from data and improve its performance over time. Upon completion of the course, students will have comprehensive knowledge of feature selection, model selection and tuning, unsupervised learning, and an introduction to neural networks.

Credits

3

Prerequisite

CMSY 231

Hours Weekly

3.5

Course Objectives

1. Evaluate the effectiveness of feature selection using metrics like accuracy, precision, recall, and F1-score.
2. Demonstrate an understanding of the trade-offs between model complexity and model performance.
3. Identify different techniques for unsupervised learning such as clustering, dimensionality reduction, and anomaly detection.
4. Demonstrate an understanding of the basic concepts of neural networks such as neurons, layers, and activation functions.
5. Build and train a simple neural network using Python and existing libraries.

Course Objectives

1. Evaluate the effectiveness of feature selection using metrics like accuracy, precision, recall, and F1-score.

   This objective is a course Goal Only

   Learning Activity Artifact

   • Other (please fill out box below)
   • Lab

   Procedure for Assessing Student Learning

   • Other (please fill out box below)
   • Lab Rubric
2. Demonstrate an understanding of the trade-offs between model complexity and model performance.

   Learning Activity Artifact

   • Other (please fill out box below)
   • Lab

   Procedure for Assessing Student Learning

   • Other (please fill out box below)
   • Lab Rubric
3. Identify different techniques for unsupervised learning such as clustering, dimensionality reduction, and anomaly detection.

   This objective is a course Goal Only

   Learning Activity Artifact

   • Other (please fill out box below)
   • Lab

   Procedure for Assessing Student Learning

   • Other (please fill out box below)
   • Lab Rubric
4. Demonstrate an understanding of the basic concepts of neural networks such as neurons, layers, and activation functions.

   This objective is a course Goal Only

   Learning Activity Artifact

   • Other (please fill out box below)
   • Lab

   Procedure for Assessing Student Learning

   • Other (please fill out box below)
   • Lab Rubric
5. Build and train a simple neural network using Python and existing libraries.

   This objective is a course Goal Only

   Learning Activity Artifact

   • Other (please fill out box below)
   • Lab

   Procedure for Assessing Student Learning

   • Other (please fill out box below)
   • Lab Rubric