Machine Learning
Objectives
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
Code
2487
Credits
7
Responsible teacher
Qiwei Han | Sabina Zejnilovic
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
n/a
Bibliography
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
Programs
Programs where the course is taught: