Course Description The course introduces machine learning with business applications. It provides a survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification. Prerequisites: MSDS 400-DL Math for Data Scientists, MSDS 401-DL Applied Statistics with R, and MSDS 402-DL Introduction to Data Science.
Objectives:
- Discuss the learning algorithm trade-offs, balancing performance within training data and robustness on unobserved test data.
- Distinguish between supervised and unsupervised learning methods.
- Differniate between regression and classification problems.
- Explain bootstrap and cross-validation procedures.
- Explore and visualize data and perform basic statistical analysis.
- List alternative methods for evaluating classifiers and regressors.
- Demonstrate the application of traditional statistical methods for classification and regression.
- Showcase Principal Components Analysis (PCA) for dimension reduction.
- Describe hierarchical and non-hierarchical clustering techniques.
- Utilize how measurement and feature engineering are relevant to modeling.
- Demonstrate the use of artificial neural networks (including deep neural networks) in classification and regression.
- Showcase how Convolutional Neural Networks (CNN) are constructed and how recurrent neural networks are constructed.
- Distinguish between autoencoders and other forms of unsupervised learning.
- Explain how the results of machine learning can be useful to business managers.
- Transform data and research results into actionable insights.