Offered: Fall 2025 Course Description: This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, and empirical evaluation. The assignments will involve both … Continue reading "COMPSCI 589 – Machine Learning – Fall 2025"

Offered: 2025 Course Description: This honors colloquium will enrich the primary course, COMPSCI 589, by focusing on reading, presenting, and discussing foundational and recent research papers from the machine learning literature. Students will write weekly reading responses, and lead one to two group discussions over the course of the semester. Prerequisite: Students must be enrolled … Continue reading "COMPSCI H589: Machine Learning Honors Colloquium – Fall 2025"

Course Description: Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundation of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing and deriving … Continue reading "COMPSCI 689: Machine Learning – Fall 2024"

Offered: 2024 Course Description: This honors colloquium will enrich the primary course, COMPSCI 589, by focusing on reading, presenting, and discussing foundational and recent research papers from the machine learning literature. Students will write weekly reading responses, and lead one to two group discussions over the course of the semester. Prerequisite: Students must be enrolled … Continue reading "COMPSCI H589: Machine Learning Honors Colloquium – Spring 2024"

Offered: 2023 Course Description: Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundation of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing … Continue reading "COMPSCI 689: Machine Learning – Fall 2023"

Offered: 2022 Course Description: Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundation of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing … Continue reading "COMPSCI 689: Machine Learning – Fall 2022"

Offered: Spring 2022 Course Description: This seminar will introduce students to research in the area of Bayesian methods applied to deep neural network models. The course will begin with foundational readings on Markov chain Monte Carlo and variational Bayesian methods and proceed to cover recent advances that are enabling the application of Bayesian inference to … Continue reading "COMPSCI 791B: Bayesian Deep Learning"

Offered: 2021 Course Description: Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundation of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing … Continue reading "COMPSCI 689: Machine Learning – Fall 2021"

Offered: 2020 Course Description: Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundation of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing … Continue reading "COMPSCI 689: Machine Learning – Fall 2020"

Semester: Spring Offered: 2012 Course Description: Probabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, … Continue reading "CMPSCI 691GM: Graphical Models-2012"