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"

Semester: Fall Offered: 2012 Course Description: Development of mathematical reasoning skills for problems that involve uncertainty. Each concept will be illustrated by real-world examples and demonstrated though in-class and homework exercises, some of which will involve Java programming. Counting and probability — basic counting problems, probability definitions, mean, variance, binomial distribution, Markov and Chebyshev bounds. Probabilistic … Continue reading "CMPSCI 240: Reasoning About Uncertainty- 2012"

Semester: Spring Offered: 2013 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 688: Probabilistic Graphical Models"