Syllabus

BANA409: Machine Learning for Business

If you do not have access to your course on Canvas, please contact: swufe-jei@udel.edu.

Time and Classroom

Schedule

Team

Professor:

• Harry Wang (harrywang.me)
• Email: hjwang@udel.edu
• Professor Office: Tongbo Building Room D205
• Professor Office Hours: By appointments

Teaching Assistants:

• Yuxi Lin: lyxiiin@udel.edu
• Kaiyun Li: kaiyunli@udel.edu
• TA Office Hours (Tongbo Room D205): TBD

About

This course introduces the basic concepts and techniques of machine learning and covers the most commonly used models for predictive analytics. This course equips business students with important machine learning skills to help organizations address challenging business problems from a data science perspective. The end-to-end workflow for typical machine learning projects is illustrated via multiple business programming cases. This course is programming intensive using Python 3 and popular packages/frameworks, such as Jupyter, Numpy, Pandas, Matplotlib, and Scikit-Learn.

NOTE: You are required to bring a laptop for every lecture.

Key Topics

• Machine Learning Overview
• Toolkit Bootcamp (Python3, uv, Jupyter, Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn)
• Supervised Learning: Classification, Regression, Ensemble Learning
• Unsupervised Learning: Clustering
• End-to-end Machine Learning Workflow
• Data App

Books and Resources

• (Free Online) Python Data Science Handbook by Jake VanderPlas: https://jakevdp.github.io/PythonDataScienceHandbook
• Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
• (Free Online) Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: https://mml-book.github.io/

Course Requirements and Policies

Requirement Categories

Individual Assignments: 70%

Exam: 30%

If a category is not assigned, I will change the weights of the other assignments on a prorated basis.

Assignments

Assignments can be in-class assignments (labs) or out-of-class assignments. Detailed assignment instructions will be given during the semester.

You must finish in-class assignments at the scheduled times. If you miss an in-class assignment, you will receive no credit (zero) for that assignment.

I accept late assignments up to one calendar day late for out-of-class assignments. Assignments submitted after the deadline but less than one day late will get a late penalty of 15%. Assignments that are more than one day late will NOT be accepted and a grade of zero will be assigned. If an assignment requires your presence in class or is offered on a credit/no credit basis, then no late assignments will be accepted.

Exam

Exams will be in-class, close-book, close-note. You must finish in-class exams at the scheduled times. If you miss an in-class exam, you will receive no credit (zero) for that exam.

Class Participation and Attendance

You are expected to attend all classes. Participation in class discussion is encouraged and highly recommended. This course will require a significant amount of your time. Good attendance can help reduce the amount of your study time after class.

Grading

Percentages are rounded to the nearest hundredth, and your final grade will be determined using the grading scale below.

A >= 93.00 A- 90.00-92.99 B+ 87.00 – 89.99 B 83.00 – 86.99 B- 80.00 – 82.99 C+ 77.00 – 79.99 C 73.00 – 76.99 C- 70.00 – 72.99 D+ 67.00 – 69.99 D 63.00 – 66.99 D- 60.00 – 62.99 F < 60.00

Academic Integrity

I assume that you have complete integrity in all your class efforts. Violations of the University's Code of Academic Integrity will be taken very seriously, and they will be addressed promptly according to the established procedures. Please review materials at https://www.udel.edu/students/support/community-standards/academic-integrity/ to understand what NOT TO DO and the potential consequences for violating academic integrity policies.

Tentative Class Schedule

• Week 1:
  • Course Overview
  • ML Toolkit Bootcamp – Basics (Python and VS Code)
• Week 1:
  • Machine Learning (ML) Overview
  • Python Basics
• Week 2:
  • Numpy and Pandas
• Week 2:
  • Pandas and Matplotlib
  • Kaggle
• Week 3:
  • Github
  • Data App (Streamlit)
• Week 3:
  • Data Cleaning and Transformation
  • Classification – Decision Tree
• Week 4: National Holiday
• Week 5:
  • Classification – Decision Tree
  • Full Pipeline
• Week 5:
  • Linear Regression
  • Gradient Descent
• Week 6: Logistic Regression and SVM
• Week 6: Ensemble Learning
• Week 7: Clustering and Final Exam