Computer Science: Advanced Machine Learning

Provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (linear regression, logistic regression, neural networks, support vector machines, decision tree, ensemble models, random forest); unsupervised learning (clustering, dimensionality reduction, kernel methods); Also introduces deep learning such as convolutional neural networks and discusses recent applications. Credit not awarded for both CS 5540 and CS 3540. Prerequisites: Knowledge of statistics as from STAT 2510, knowledge of linear algebra as from MATH 2522 or MATH 2544; Graduate student.

Prereqs enforced by the system:STAT 2510 & Math 2522 or 2544 Co-located with CS 3540 A and CMPE 3990 A; Cross-listed with CSYS 5990; Total combined enrollment: 45; Open to Degree and PACE students

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 (25%) • Term Project (35%) • Mid term exams (30%). To exams will be given during the semester, no final exam. • Class attendance and participation (10%).