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 … Continue reading "COMPSCI 689: 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 … Continue reading "COMPSCI 589/589HH: Machine Learning – 2017"

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 … Continue reading "COMPSCI 589/589HH: Machine Learning – 2016"

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 … Continue reading "CMPSCI 240 – Reasoning About Uncertainty-2015"

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 Lecture Notes Python Scientific Notes SageMathCloud Python Distros (free Anaconda and Canopy distros are recommended) intro-day1.ipynb (direct download) exercises-day1.ipynb (direct download) intro-day1.ipynb (shared on SageMathCloud) … Continue reading "2015 REUMass Amherst Data Science Bootcamp"

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 … Continue reading "CMPSCI 589 – Machine Learning-2015"

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. As a result, … Continue reading "CMPSCI 688: Probabilistic Graphical Models-2014"