Introduction to Machine Learning and Machine Learning Systems

Authors

• Raffi T. Khatchadourian Ph.D., CUNY Hunter CollegeFollow

Document Type

Lecture or Presentation

Publication Date

Fall 11-26-2025

Abstract

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon University).

Comments

https://github.com/CSCI-40500-Fall-2025/ML-Intro

Creative Commons License

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.