Introduction
Introduction to the course
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What you should know
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1. Overview of OpenAI o-Series
What are the o-Series models? How are they different?
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From System-1 to System-2 “thinking” models
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Training scaling laws
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The paradigm shift in AI with inference scaling
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2. Advanced Reasoning Capabilities
Chain-of-thought reasoning
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Majority voting at test-time
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Benchmark comparison: Model performance in math, coding, and science
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Abstract reasoning
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3. Practical Applications and Hands-On Exercises
API features for developers
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Best practices for prompt engineering with o-series
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How to set up the lab files
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Lab 1: Prompt engineering with reasoning models
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Software development
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Lab 2: Game development with reasoning models
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Document risk analysis
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Lab 3: Fraud detection with reasoning models
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Constraints satisfaction problem
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Lab 4: Employee scheduling with reasoning models
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Visual reasoning
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Lab 5.1: Complex floor plan analysis with reasoning models lab 5.2 ERD analysis, SQL generation, synthetic data generation
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Evaluation and benchmarking
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Understanding and controlling costs
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4.Understanding Safety and Security
Security threats and limitations
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Building a safer and responsible AI solution
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5. The Next Frontier: What Lies Ahead with Reasoning Models
DeepSeek: A powerful open source alternative
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Lab 6: Working with DeepSeek
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GPT-5 and beyond
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Future outlook
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Conclusion
Conclusion
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Acknowledgements
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