CEEM586 - Neural Networks and Machine Learning

Course Description

Neural networks, a nonlinear supervised learning modeling tool, have become hugely popular within the last two decades because they have been successfully applied to a wide range of problems, including automatic language processing, image classification, object detection, speech recognition, and pattern recognition. They are mathematical models that are loosely built up based on an analogy to the interconnected neuron in the brain. They take in a vector or matrix of input data and output either a classification value or an approximation to a functional value. The beauty is that the relationships between the inputs and outputs can be highly non-linear and complex.

In this course, you will explore the mechanics of neural networks and the intricacies involved in fitting them to data for prediction. Using packages in the free and open-source statistical programming language R with real-world data sets, you will implement these techniques. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Understanding Data Analytics
• Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
• Finding Patterns in Data Using Cluster and Hotspot Analysis
• Regression Analysis and Discrete Choice Models
• Supervised Learning Techniques

Faculty Author

Benefits to the Learner

• Examine common architectures and activation functions for neural networks
• Identify how to optimize the parameters in a neural network
• Make predictions using neural networks in R
• Practice deep learning using R
• Apply ideas for cross-validation for neural network model development and validation
• Tune parameters in a neural network using a grid search
• Use the package Lime in R to recognize which variables are driving the recommendations your neural network is making

Target Audience

• Current and aspiring data scientists
• Analysts
• Engineers
• Researchers
• Technical managers