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5.0
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5.0
Difficulty
1,000
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99%
Would take again
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Not teaching in Spring 2026 | |||||
FIN 6327 Zhiqiang Zheng | |||||
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Not teaching in Spring 2026 | |||||
FIN 6327 Zhiqiang Zheng | |||||
Grades: 273
Median GPA: B+
Mean GPA: 3.435
3.4
Professor rating
3.5
Difficulty
8
Ratings given
63%
Would take again
Advanced AI Techniques for Financial Applications
FIN 6327
Naveen Jindal School of Management
This course covers the application of advanced AI technologies to financial data. Students gain a deep understanding of advanced AI techniques with a focus on their application in reshaping financial services such as risk assessment and asset management. Topics include, but are not limited to, LLM and GPT foundations, transformer models in financial market analysis, diffusion models for risk assessment, prompt engineering, retrieval-augmented generation (RAG), reinforcement learning and fine tuning, GenAI for digital assets, multi-modal analyses, and responsible AI in finance. The class is hands-on and Python-based. Recommended Prerequisite: Working knowledge of Python is preferred. 3 credit hours.
Offering Frequency: Each year
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Grades: 273
Median GPA: B+
Mean GPA: 3.435
3.4
Professor rating
3.5
Difficulty
8
Ratings given
63%
Would take again
Advanced AI Techniques for Financial Applications
FIN 6327
Naveen Jindal School of Management
This course covers the application of advanced AI technologies to financial data. Students gain a deep understanding of advanced AI techniques with a focus on their application in reshaping financial services such as risk assessment and asset management. Topics include, but are not limited to, LLM and GPT foundations, transformer models in financial market analysis, diffusion models for risk assessment, prompt engineering, retrieval-augmented generation (RAG), reinforcement learning and fine tuning, GenAI for digital assets, multi-modal analyses, and responsible AI in finance. The class is hands-on and Python-based. Recommended Prerequisite: Working knowledge of Python is preferred. 3 credit hours.
Offering Frequency: Each year
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.