The rapid advancement of computational methods from machine/statistical learning, data mining, and pattern recognition provides unprecedented opportunities for understanding large, complex datasets. This course takes a practical approach to introduce several machine learning methods with business applications in marketing, finance, and other areas. The course aims to provide a practical survey of modern machine learning techniques that can be applied to make informed business decisions: regression and classification methods, resampling methods and model selection, regularization, perceptron and artificial neural networks, tree-based methods, support vector machines and kernel methods, principal components analysis, and clustering methods.
At the end of this course, students will have a basic understanding of how each of these methods learn from data to find underlying patterns useful for prediction, classification, and exploratory data analysis. Further, each student will learn how to implement machine learning methods in the R statistical programming language for improved decision-making in real business situations.
The course format is a combination of textbook readings and lecture slides, R Lab video sessions, and group discussions. Weekly quizzes and programming assignments using R will be used to reinforce both machine learning concepts and practice. The final project will involve students applying multiple machine learning methods to solve a practical business problem in marketing.
- Demonstrate a practical understanding of the key theoretical concepts of modern computational/ analytic methods from machine/statistical learning, data mining, and pattern recognition.
- Identify appropriate machine learning methods to find relationships and structure in data with and without specific output variable(s).
- Apply machine learning methods to build predictive models and discover patterns in data for more informative business decision-making.
- Develop analytic solutions to practical business problems using the R statistical programming language, transforming data into knowledge.
Required Textbook: An Introduction to Statistical Learning, with Applications in R (2013), by G. James, D. Witten, T. Hastie, and R. Tibshirani.
Note: This textbook is available for free download at http://www-bcf.usc.edu/~gareth/ISL/.
Statistical Software: R, which can be downloaded for free from http://www.r-project.org. Rstudio is the recommended interface for the R statistical programming language software, which can also be downloaded for free at http://www.rstudio.org.
Please refer to the course syllabus for more details:COURSE SYLLABUS.pdf
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