ECE 5420 - Fundamentals of Machine Learning

Fall, Spring. 4 credits. Student option grading.

Prerequisite: MATH 2940, ECE 3100 or STSCI 3080 or ECE 3250 or equivalents. Enrollment limited to: graduate students. Co-meets with ECE 4200.

J. Acharya.

The course will be devoted to understanding, implementation, and applications of various machine learning primitives. This course is intended to have three modules, and within each we will cover basic theory, and implementations. The modules will be supervised learning, unsupervised learning, and finally topics that are motivated by engineering applications such as speech recognition, and recommendation systems. Supervised learning will include regression, support vector machines, decision trees, random forests, naïve Bayes, boosting and bagging. Unsupervised learning includes clustering, k-means, k-NN, principal components analysis and other dimensionality reduction methods. We will give particular emphasis on engineering applications, e.g., text data, hand-writing, music, image, and time series data, and categorical datasets such those in recommendation systems. The course will have a programming component, which will be administered in the form of assignments, and in-class-kaggle competition

Extra homework problems, report required for projects.