Course Syllabus

MATH 3480 Machine Learning

Course Description

This course introduces the theory and application of machine learning, sometimes referred to as artificial intelligence. Students who take this course will understand and be able to deploy basic supervised and unsupervised learning techniques including but not limited to decision trees, neural networks, support vector machines, and other commonly-used machine learning models. Students will also be introduced to natural language processing models.

Justification

Artificial intelligence is becoming integrated with all aspects of everyday life. It has become a central aspect of not only daily life but of our economy as well. MATH 3480 is the 3rd in a 3-course series on Data Science. This course focuses on a variety of supervised and unsupervised machine learning models.

Student Learning Outcomes

1. Upon successful completion of this course students will be able to accurately perform exploratory data analysis and preprocessing on datasets, preparing the data to train models
2. Upon successful completion of this course students will be able to use common machine learning libraries in a popular computing language (e.g., Python, R, etc)
3. Upon successful completion of this course students will be able to distinguish between supervised and unsupervised models and determine the appropriate use of each model
4. Upon successful completion of this course students will be able to describe the mathematical theory behind common machine learning techniques
5. Upon successful completion of this course students will be able to utilize different models to make predictions, then use appropriate performance metrics to evaluate the quality of those predictions

Course Content

This course will include several fundamental supervised/unsupervised learning algorithms including decision trees, perceptrons, neural networks, kernel methods, support vector machines, and probabilistic methods like Bayesian networks.