Teaching

COMPSCI 689: Machine Learning

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

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

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

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.

Day 1: Introduction

CMPSCI 589 - Machine Learning

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

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

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

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.