Machine Learning

About

The main goal of the course is to teach students basic concepts and methods of machine learning in order to better understand data and make decisions in the world. The students will gain knowledge in using a variety of techniques and algorithms to analyze data to make predictions about future events or to discover meaningful patterns and rules.

The software to be used in this class is Python and R. The focus of the course, however, will be on teaching machine learning algorithms and applications rather than how to use Python and R. Students will be able to use Python packages (numpy, pandas, scikit-learn, matplotlib) to solve and visualize machine learning problems.

Topics covered:

Machine Learning (Basic Concepts), Supervised learning, Unsupervised learning, Introduction to Python (R)
Prediction Accuracy, Model Interpretability, Regression versus Classification problems, Assessing Model Accuracy
Regression (Multiple linear regression, K-nearest neighbors)
Classification (Logistic Regression)
Classification (Linear Discriminant Analysis, Quadratic Discriminant Analysis, K-Nearest Neighbors)
Resampling Methods (Cross-Validation, Bootstrap)
Linear Model Selection and Regularization (Subset Selection, Ridge Regression, Lasso Regression)
Linear Model Selection and Regularization (Principal Components Regression, Partial Least Squares)
Regression Splines, Smoothing Splines
Generalized Additive Models
Decision Trees, Bagging, Random Forests, Boosting
Support Vector Machines
Unsupervised Learning (Principal Components Analysis, Clustering Methods)

Course leader

Denis Lukić, Ph.D., FRM, Senior Risk Management Specialist

Target group

Undergraduate and graduate students of business, economics or any related field.

Fee info