ISL 439E - Introduction to Machine Learning with Business Applications

Course Objectives

1. to introduce students several fundamental concepts and methods for machine learning.
2. to familiarize the audience with learning algorithms and their applications in business environments.
3. to develop an understanding of the modern data analysis, strengths and weaknesses of many popular machine learning approaches.

Course Description

Introduction to machine learning, applications of machine learning, introduction to programming with R, supervised and unsupervised learning, linear regression and its extensions, classification overview, logistic regression, linear discriminant analysis, using Bayes’ Theorem for classification, Naive Bayes technique, K-nearest neighbors approach, resampling methods, cross validation, Ridge regression, Lasso regression, tree based methods, random forest technique, boosted regression and classification, support vector machines, neural networks, an overview of deep learning, unsupervised learning methods, principal components analysis, K-means clustering, hierarchical clustering, Apriori algorithm, market basket analyses.