Bridging Traditional Machine Learning and Large Language Models: A Two-Part Course Design for Modern AI Education

TL;DR

The paper tackles the challenge of educating AI practitioners to bridge traditional machine learning with large language models. It introduces a two-part, sequential course design spanning two seven-week terms: Part 1 foundational ML and Part 2 LLM applications. It detailing curriculum architecture, Colab-based implementation, assessment strategies, and pilot outcomes, showing improved comprehension and industry readiness. The work contributes explicit conceptual bridging between paradigms, integration of professional software practices, and a scalable blueprint for AI education amid rapid technological change.

Abstract

This paper presents an innovative pedagogical approach for teaching artificial intelligence and data science that systematically bridges traditional machine learning techniques with modern Large Language Models (LLMs). We describe a course structured in two sequential and complementary parts: foundational machine learning concepts and contemporary LLM applications. This design enables students to develop a comprehensive understanding of AI evolution while building practical skills with both established and cutting-edge technologies. We detail the course architecture, implementation strategies, assessment methods, and learning outcomes from our summer course delivery spanning two seven-week terms. Our findings demonstrate that this integrated approach enhances student comprehension of the AI landscape and better prepares them for industry demands in the rapidly evolving field of artificial intelligence.