Computer Science: Machine Learning

Introduction to machine learning algorithms, theory, and implementation, including supervised and unsupervised learning; topics typically include linear and logistic regression, learning theory, support vector machines, decision trees, backpropagation artificial neural networks, and an introduction to deep learning. Includes a team-based project. Prerequisites: STAT 2510 or STAT 5510; MATH 2522 or MATH 2544.

Prereqs enforced by the system: STAT 2510 or 5510, and MATH 2522 or 2544; Colocated with CS 5540 OL1 and CSYS 5990 OL2; Total combined enrollment: 45 Open to Degree and PACE students; Online Asynchronous;

This course offers an introduction to advanced machine learning and statistical pattern recognition algorithms. It includes a wide array of topics, including supervised learning (covering linear regression, logistic regression, neural networks, support vector machines, decision trees, ensemble models, and random forests), as well as unsupervised learning (clustering, dimensionality reduction, and kernel methods). In addition, the course delves into the introduction of deep learning, featuring convolutional neural networks, and explores the latest applications of machine learning. You'll also gain valuable insights and guidance on effectively implementing machine learning algorithms.

Weekly assignments, Term Project and two Mid-term exams.

• Assigned homework (30%) • Term Project (30%) • Mid term exams (30%). To exams will be given during the semester, no final exam. • Class attendance and participation (10%).