Semester: Fall Offered: 2013 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-2013"

Semester: Spring Offered: 2013 Course Description: This seminar will focus on models and algorithms for supervised and unsupervised machine learning with time series. Topics will include discrete and continuous time models from machine learning, statistics and econometrics. We will investigate a variety of time series problems including prediction, detection, clustering, and similarity search. Coursework for the … Continue reading "CMPSCI 791TS: Machine Learning and Time Series-2013"

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"

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: 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"

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"

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: 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: 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"