Aims and Objectives

In recent years, machine learning techniques have been successfully applied to a wide range of problems across fields such as Economics, Finance, and Management. This course is designed to introduce fundamental machine learning methods and provide a critical understanding of their strengths and limitations to Management Information Systems students. Topics include commonly used techniques such as classification, regression, clustering and black-box models.

At the end of the course, the students shall acquire basic knowledge about widely used machine learning techniques. Course is an applied course, hence, students will be able to apply these techniques and assess them. They will become familiar with the pros and cons of applying these techniques. The main software used in this course for statistical programming is R. Students shall be able to use R and its related packages. The content of the course is dynamic and changing in every year.

Course Content and Schedule

The content of the course includes (but not limited):

Week Theme Key Competencies
1 Introduction Content of the course
1, 2 Why, Installing R, RStudio, Rstudio 101 Environment setup on Windows, CRAN
3 Statistical Learning: an Intro Learning from data, Variance-Bias Tradeoff
4 Data - Model - Analysis Data Types, Exploratory Data Analysis, Summaries, Visualization
5,6 Review: Regression, Dependent: Real Valued, Review: Regression, Dependent: Categorical Regression Review: Dependent Variable Real/Categorical
7 Review: Cross Validation, Regularization Cross-Validation, Polynomial Regression, Model Selection, Ridge Regression, LASSO Regression, Elastic Net
8 Spline Regression, GAM Splines, Generalized Additive Models
8 Review: Decision Trees, Model Based Trees Decision Trees, Model Based Trees
9 Bagging, Random Forest, Boosting Bagging, Boosting, Random Forest, Extreme Gradient Boosting
10 Review: Similarity, Introduction to Clustering Similarity measures, Clustering algorithms
11 Introduction to Clustering K-means clustering, Hierarchical Clustering
12 Introduction to Neural Networks Neural Networks
13 Term Project Presentations
14 Term Project Presentations

Course Materials

Textbook


Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R (Second Edition)

Software:

R, RStudio (download links and setup instructions will be provided), and relevant R packages.

References and Suggested Readings


Bradley Boehmke & Brandon Greenwell, Hands-On Machine Learning with R, 2020-02-01

J. Hull, Machine Learning in Business: An Introduction to the World of Data Science


Articles (to be distributed), Lecture Notes (from other universities)

Evaluation Criteria

Policies

Academic integrity is fundamental to the academic mission of the university. Acts of academic dishonesty, including but not limited to plagiarism, cheating, fabrication, or unauthorized collaboration, undermine the learning process and violate university policies.

Specific guidelines include:

  1. Plagiarism: Using someone else’s work, ideas, or words without proper attribution is strictly prohibited. This includes copying and pasting from any source, paraphrasing without citation, or submitting another person’s work as your own.

  2. Cheating: Unauthorized use of materials, devices, or information during exams or assignments, including sharing or receiving answers, is not allowed.

  3. Fabrication: Falsifying or inventing data, citations, or research is a breach of academic integrity.

  4. Collaboration: While collaboration on group assignments may be permitted, sharing answers or work on individual tasks is not acceptable unless explicitly authorized.

  5. Consequences: Violations of academic integrity will be addressed following the university’s academic policies, potentially leading to penalties such as assignment failure, course failure, or further disciplinary actions.