Machine Learning

Catalog description

In this class, students will learn about the theoretical foundations of machine learning and how to apply these to solve real-world data-driven problems. We will apply machine learning to numerical, textual, and image data. Topics will be drawn from perceptron algorithm, regression, gradient descent and stochastic gradient descent, support vector machines, kernels for support vector machines, recommendation systems, decision trees and random forests, maximum likelihood, estimation, logistic regression, neural networks and the back propagation algorithm, convolutional neural networks, recurrent neural networks, Bayesian analysis and naive Bayes, clustering, latent Dirichlet allocation (LDA), sentiment analysis, dimensionality reduction and principle component analysis, reinforcement learning. Prerequisites: Introduction to Computer Programming, Calculus, and (Probability and Statistics OR Theory of Probability OR Statistics for Business & Economics OR Linear Algebra). Fulfillment: Business Analytics Track; Computer Science Electives; Data Science Major Data Analysis Courses. Equivalencies: CSCI-UA 473 , CSCI-UA 9473