AI Quantitative Methods

AI Quantitative Methods explores the essential mathematical and statistical foundations underpinning artificial intelligence, progressing through machine learning fundamentals to advanced quantitative techniques and practical applications. The book begins with foundational topics such as linear algebra, probability, optimization, and information theory, providing the rigorous tools necessary to understand AI models. It then dives into core machine learning concepts, including supervised and unsupervised learning, evaluation metrics, probabilistic models, and deep learning architectures, emphasizing the quantitative reasoning behind algorithm design and performance assessment.

The advanced section addresses specialized topics like Bayesian machine learning, time series forecasting, reinforcement learning, causal inference, and game theory, highlighting how quantitative methods facilitate robust AI solutions in complex, dynamic environments. The final part connects theory with real-world applications across natural language processing, computer vision, financial modeling, operations research, and ethics in AI. It shows how quantitative techniques optimize decision-making, improve predictive accuracy, and ensure fairness and explainability in AI systems.

Throughout, the book emphasizes detailed mathematical formulations and algorithmic insights without unnecessary introductions or summaries, targeting readers seeking deep technical understanding. By blending theory with practical examples, it equips data scientists, AI researchers, and quantitative analysts with the tools to develop, evaluate, and deploy AI systems effectively across diverse domains.

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