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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.05167 |
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| _version_ | 1866918232296783872 |
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| author | Li, Fang |
| author_facet | Li, Fang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Traditional Machine Learning and Large Language Models: A Two-Part Course Design for Modern AI Education Li, Fang Artificial Intelligence 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. |
| title | Bridging Traditional Machine Learning and Large Language Models: A Two-Part Course Design for Modern AI Education |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.05167 |