CS 38300 - Machine Learning

This course is an introduction to both theory and applications in machine learning. The topics include the fundamentals of machine learning, end-to-end machine learning projects, and popular machine learning methods and technologies, such as linear regression, gradient descent, polynomial regression, logistic regression, support vector machines, decision tree, random forest, artificial neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and unsupervised learning techniques.

Preparation for Course
P: CS 26000 with a grade of C or better, and either STAT 51100 or STAT 30100.

Cr. 3.
Student Learning Outcomes
1. Understand the concepts of supervised learning, unsupervised learning, self-supervised learning, and reinforcement learning. (6)
2. Understand the workflow and the procedure of developing an end-to-end machine learning project. (1, 2, 6)
3. Apply different machine learning methods (e.g., logistic regression, support vector machine, decision tree, and random forest) to solve data science problems. (1, 2, 6)
4. Apply proper deep learning models (e.g., artificial neural network, convolutional neural network, and recurrent neural network) to different types of data science applications. (1, 2, 6)
5. Apply proper measures to evaluation the performance of a machine learning method in both classification and regression data science problems. (1, 2, 6)
6. Work with a team to apply machine learning algorithms to solve data science problems. (1, 2, 5, 6)

(numbers in paratheses = ABET program outcomes)