QuantFin a republié ceci
𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐟𝐢𝐧𝐚𝐧𝐜𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 - 𝐁𝐨𝐨𝐤 𝟑 📘 Machine Learning in Finance by Matthew F. Dixon, Igor Halperin, & Paul Bilokon 📘 🔍 From the Back Cover: This book introduces machine learning methods in finance, presenting a unified treatment of ML and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control. Emphasis is placed on how theory and hypothesis tests inform the choice of algorithms for financial data modelling and decision making. 📊💡 With the trend towards increasing computational resources and larger datasets, machine learning has become a crucial skillset for the finance industry. 📈💻 This book is written for advanced graduate students, academics in financial econometrics, mathematical finance, and applied statistics, as well as quants and data scientists in quantitative finance. 🎓🔬 📖 Machine Learning in Finance: From Theory to Practice 📖 The book is divided into three parts, each covering theory and applications: Supervised Learning for Cross-Sectional Data 📈: - Covers Bayesian and frequentist perspectives. - Advanced material on neural networks (including deep learning) and Gaussian processes. Supervised Learning for Time Series Data ⏳: Focuses on the most common data type used in finance. Examples in trading, stochastic volatility, and fixed income modeling. Reinforcement Learning 🔄: Applications in trading, investment, and wealth management. Python code examples to support understanding of methodologies and applications. #MachineLearning #Finance #QuantitativeFinance #DataScience #ML #Python #TensorFlow #ReinforcementLearning #InvestmentManagement #TradingStrategie