Machine Learning


This course will be an introduction to machine learning techniques and how to use them to help solve business problems. This course is designed for management, economics and finance students who are interested in learning modern, scalable, computational data analysis methods also known as machine learning, and apply them to business and social problems. This is a hands-on course where students will be expected to use Python to implement solutions to various business problems. Prior experience with Python is required, and students should be familiar with basics Python data science libraries such as numpy, pandas, matplotlib, seaborn. Having additional programming knowledge, such as R, Matlab, Java, etc. would be highly useful. 

General characterization





Responsible teacher

Qiwei Han | Sabina Zejnilovic


Weekly - Available soon

Total - Available soon

Teaching language





This course does not require any textbook, because data science is a rapidly changing field and no textbook may cover all materials we will teach in the course.

However, the following book is recommended for your reference:

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Python Data Science Handbook Essential Tools for Working with Data

Introduction to Machine Learning with Python

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition

Hands-on Machine Learning with scikit-learn, Keras, and Tensorflow 

Teaching method

Students are required to bring own laptops for in-class exercises and quizzes. This course adopts learning-by-doing culture that allows students to implement machine learning pipeline through programming in Python. Most of class material will be in the Jupyter notebooks to facilitate reproducible practices. 


Evaluation method

The overall evaluation of performance consists of 4 parts:

Class participation through 6 quizzes (10%) 4 tri-weekly assignment (20%) Course project (30%) Final exam (40%) 

Subject matter

Overview of the Machine Learning Process

Machine Learning Methods

Feature creation, feature engineering and feature selection

Interpretability and transparency

Ethics, Fairness and Bias