Teaching

COMPSCI 689: Machine Learning – Fall 2022

Semester: Fall
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 and deriving learning algorithms for a variety of models from first principles. 3 credits.

COMPSCI 791B: Bayesian Deep Learning – Spring 2022

Semester: Spring
Offered: 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 increasingly large deep learning models. The course will also cover methods for accelerating prediction using Bayesian deep learning models and for evaluating Bayesian deep learning models. Students will need background in deep learning (such as provided by COMPSCI 682 or COMPSCI 689) and probabilistic graphical models (such as provided by COMPSCI 688). The seminar will focus on reading, presenting, and discussing classical and recent papers (1 credit) and a final project focusing on a Bayesian deep learning topic (3 credits). 1-3 credits.

COMPSCI 689: Machine Learning – Fall 2021

Semester: Fall
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 and deriving learning algorithms for a variety of models from first principles. 3 credits.

COMPSCI 689: Machine Learning – Fall 2020

Semester: Fall
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 and deriving learning algorithms for a variety of models from first principles. 3 credits.

COMPSCI 689: Machine Learning – Fall 2019

Semester: Fall
Offered: 2019
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 learning algorithms for a variety of models from first principles. 3 credits.

COMPSCI 689: Machine Learning – Fall 2018

Semester: Fall
Offered: 2018
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 learning algorithms for a variety of models from first principles. 3 credits.

STATISTC/COMPSCI 190F: Foundations of Data Science – Fall 2018

Semester: Fall
Offered: 2018
Course Description: The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks.

CMPSCI 688: Probabilistic Graphical Models – Spring 2018

Semester: Spring
Offered: 2018
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.

COMPSCI 689: Machine Learning – Fall 2017

Semester: Fall
Offered: 2017
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 learning algorithms for a variety of models from first principles. 3 credits.

COMPSCI 589/589HH: Machine Learning – Spring 2017

Semester: Spring
Offered: 2017
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 understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results.

COMPSCI 589/589HH: Machine Learning-2016

Semester: Spring
Offered: 2016
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 understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results.

CMPSCI 240 – Reasoning About Uncertainty-2015

Semester: Fall
Offered: 2015
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 reasoning — conditional probability and odds, Bayes’ Law, Naive Bayes classifiers, Monte Carlo simulation.

2015 REUMass Amherst Data Science Bootcamp

Semester: Spring
Offered: 2015
This course is a short introduction to data science with a focus on machine learning and Python. It is offered as part of the 2015 REUMass Amherst Data Science summer program.

CMPSCI 589 – Machine Learning-2015

Semester: Spring
Offered: 2015
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 understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results.

CMPSCI 688: Probabilistic Graphical Models-2014

Semester: Spring
Offered: 2014
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.

CMPSCI 240: Reasoning About Uncertainty-2013

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 reasoning — conditional probability and odds, Bayes’ Law, Naive Bayes classifiers, Monte Carlo simulation.

CMPSCI 791TS: Machine Learning and Time Series

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 one credit option will include paper presentations and quizzes. Students in the three credit option will also complete a course project.

CMPSCI 688: Probabilistic Graphical Models-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.

CMPSCI 240: Reasoning About Uncertainty

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 reasoning — conditional probability and odds, Bayes’ Law, Naive Bayes classifiers, Monte Carlo simulation.

CMPSCI 691GM: Graphical Models

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.