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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-finance/
课程评论：没有评论
课程名称：金融工程与人工智能在Python中的应用 课程概述：您是否曾想过如果将机器学习和人工智能与金融工程的力量结合在一起会发生什么？现在，您可以停止想象，开始实践。本课程将教您金融工程的核心基础知识，并融入机器学习的视角。我们将涵盖金融工程中必备的主题，如： - 探索性数据分析、显著性检验、相关性、阿尔法和贝塔 - 时间序列分析、简单移动平均、指数加权移动平均 - 霍尔特-温特斯指数平滑模型 - ARIMA与SARIMA - 有效市场假说 - 随机游走假说 - 时间序列预测（“股票价格预测”） - 现代投资组合理论 - 有效边界/马克维茨前沿 - 均值-方差优化 - 最大化夏普比率 - 使用线性规划和二次规划的凸优化 - 资本资产定价模型（CAPM） - 算法交易（VIP专享） - 统计因子模型（VIP专享） - 隐马尔可夫模型下的状态检测（VIP专享） 此外，我们将探索一些来自机器学习和人工智能领域的非传统技术，如： - 回归模型 - 分类模型 - 无监督学习 - 强化学习与Q学习 *** VIP专享部分（限时提供） *** - 算法交易（趋势跟随、基于机器学习和Q学习的策略） - 统计因子模型 - 使用HMM建模波动率聚类的状态检测 您将了解到过去十年市场营销人员自称“机器学习专家”所犯下的最大错误，他们承诺教无辜学生如何“用LSTM预测股票价格”。您将学习到，为什么他们的方法论根本存在缺陷，为什么他们的结果完全错误。这是一个如何不在金融领域应用人工智能的教训。 作为近30门机器学习、深度学习、数据科学和人工智能课程的作者，我无法抗拒深入复杂而广阔的金融工程世界。本课程适合任何热爱金融或人工智能的人，尤其是同时喜欢这两者的您！无论您是学生、专业人士还是想要职业进阶的人，这门课程都适合您。 感谢您的阅读，期待在课堂上见到您！ 建议预备知识： - 矩阵运算 - 概率 - 良好的Python编程技能 - Numpy、Matplotlib、Scipy和Pandas（我会免费教您，无需借口！） 该课程的独特特点： - 每行代码详细解释 - 如果您有任何异议，可以随时给我发邮件 - 没有浪费时间在键盘上“打字”，我们都知道没有人能在20分钟内从零开始写出值得学习的代码 - 不畏惧大学级数学 - 了解其他课程遗漏的重要算法细节
Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?Today, you can stop imagining, and start doing.This course will teach you the core fundamentals of financial engineering, with a machine learning twist.We will cover must-know topics in financial engineering, such as:Exploratory data analysis, significance testing, correlations, alpha and betaTime series analysis, simple moving average, exponentially-weighted moving averageHolt-Winters exponential smoothing modelARIMA and SARIMAEfficient Market HypothesisRandom Walk HypothesisTime series forecasting ("stock price prediction")Modern portfolio theoryEfficient frontier / Markowitz bulletMean-variance optimizationMaximizing the Sharpe ratioConvex optimization with Linear Programming and Quadratic ProgrammingCapital Asset Pricing Model (CAPM)Algorithmic trading (VIP only)Statistical Factor Models (VIP only)Regime Detection with Hidden Markov Models (VIP only)In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:Regression modelsClassification modelsUnsupervised learningReinforcement learning and Q-learning***VIP-only sections (get it while it lasts!) ***Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies)Statistical factor modelsRegime detection and modeling volatility clustering with HMMsWe will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but wander into the vast and complex world of financial engineering.This course is for anyone who loves finance or artificial intelligence, and especially if you love both!Whether you are a student, a professional, or someone who wants to advance their career - this course is for you.Thanks for reading, I will see you in class!Suggested Prerequisites:Matrix arithmeticProbabilityDecent Python coding skillsNumpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
