Machine Learning

Catalog description

This course introduces the fundamental concepts and algorithms of machine learning (ML) with their implementations and applications to practical problems consisting of modeling and prediction. Students will gain knowledge on the formulation of learning representation, over-fitting, generalization; clustering, classification, regression, and probabilistic modeling. Topics include linear regression, supervised and unsupervised learning, dimensionality reduction, Naïve Bayes, feedforward neural networks, deep convolutional neural networks, and the state-of-the-art machine learning libraries. Permission of instructor required. Prerequisite(s): (ENGR 15100 FOR LEVEL UG WITH MIN. GRADE OF D- OR ECE 15200 FOR LEVEL UG WITH MIN. GRADE OF D-) AND MA 26100 FOR LEVEL UG WITH MIN. GRADE OF D- AND MA 26500 FOR LEVEL UG WITH MIN. GRADE OF D- AND ECE 30200 FOR LEVEL UG WITH MIN. GRADE OF D- (MAY BE TAKEN CONCURRENTLY) Course Learning Outcomes 1. Identify the definitions of and differences among artificial intelligence (AI), machine learning (ML), deep learning, data science, and other related terminology. 2. Solve simple machine learning problems in clustering, classification, and regression mathematically by hand. 3. Use built-in Python libraries to solve elaborate machine learning problems using supervised and unsupervised learning processes. 4. Develop MATLAB software packages for applying clustering, regression, and classification concepts algorithmically. 5. Apply machine learning to a variety of real-world problems. View Class Schedule