ISYS 574 Artificial Intelligence (AI)/Machine Learning (ML) for Business Applications

Fall 2025

Zoom MeetingTime:  T 6:30-9:15 PM

Virtual Office Hours: T 5:30-6:30 PM

Email: sgill@sfsu.edu

The primary objective of this course is to develop students’ knowledge of Machine Learning (ML) in a project-based way, touching on a broad range of topics from the basics to the latest deep learning techniques. In addition, it covers topics including supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and reinforcement learning. The course applies ML models to practical business problems such as predicting the price of a car, determining whether a customer is going to churn, assessing the risk of not returning a loan, and classifying images of clothes. The course also includes best practices for using artificial intelligence and machine learning techniques to extract meaningful information from data to help form an ML-for-business mindset. The course presents ethical consideration in the application of AI/ML techniques in business.

You can get the syllabus here.

 

Upon completion of this course, students should gain an understanding of and ability to:

  • Knowledge of essential AI concepts and technologies, such as machine learning, deep learning, and natural language processing.
  • Understand the use of the Python language and the standard PyData stack: NumPy, SciPy, Pandas, and Scikit-Learn, as well as the use of other libraries, like Keras with TensorFlow for deep learning.
  • Learn the use of tools for machine learning for business including jupyter notebooks using Anaconda and/or cloud platforms
  • Understand the use of machine learning to solve the business problems such as:
    • Predicting the price of a car
    • Determining whether a customer is going to churn
    • Assessing the risk of not returning a loan
    • Classifying images of clothes
  • Learn about data preparation and creating data sets for training, validation, and testing.
  • Understand the use of unsupervised learning techniques in tandem with Python libraries to extract meaningful information from unstructured data.
  • Understand the best practices for using supervised, unsupervised, semi-supervised, and reinforced machine learning and their application to business problems.
  • Ethical framework for harmonized rules on Artificial Intelligence
  • Understand the use of generative AI, a cutting-edge technology for generating synthetic (yet strikingly realistic) data using advanced machine learning algorithms.