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Fundamentals of Machine Learning in Finance

Курс от New York University
Средний≈ 18.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

Навыки, которые вы освоите

Portfolio ManagementDimensionality ReductionFinancial TradingDecision Tree LearningCorrelation AnalysisApplied Machine LearningFinancial ServicesReinforcement LearningSupervised LearningMachine Learning AlgorithmsScikit Learn (Machine Learning Library)Machine Learning MethodsArtificial Neural NetworksExploratory Data AnalysisFinancial MarketMachine Learning SoftwareMachine LearningPython ProgrammingMarket DataUnsupervised Learning

Программа курса

4 модулей · 54 учебных материалов

01Fundamentals of Supervised Learning in Finance15 материалов

Machine Learning in Finance: Review

What is Machine Learning in Finance?ВидеоIntroduction to Fundamentals of Machine Learning in FinanceВидео

Lecture 1. Support Vector Machines

Support Vector Machines, Part 1ВидеоSupport Vector Machines, Part 2Видео

Учитесь у экспертов

Igor Halperin

Преподаватель курса

Fundamentals of Machine Learning in Finance
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Обучение на Coursera

≈ 18.2 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
SVM. The Kernel TrickВидео
Example: SVM for Prediction of Credit SpreadsВидео
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 2004Чтение

Lecture 2. Supervised Learning. Tree methods

Tree Methods. CART TreesВидеоTree Methods: Random ForestsВидеоTree Methods: BoostingВидеоA. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 7ЧтениеK. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.4Чтение

Module 1 Assessment

Jupyter Notebook FAQЧтениеRandom Forests And Decision TreesЛабораторнаяRandom Forests And Decision TreesПрограммирование
02Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction11 материалов

Core Concepts of Unsupervised Learning

Core Concepts of ULВидео

Principal Component Analysis for Stock Returns

PCA for Stock Returns, Part 1ВидеоPCA for Stock Returns, Part 2ВидеоC. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.1Чтение

Dimension Reduction

Dimension Reduction with PCAВидеоDimension Reduction with tSNEВидеоDimension Reduction with AutoencodersВидеоA. Geron, “Hands-On ML”, Chapters 8 & 15Чтение

Module 2 Assessment

Jupyter Notebook FAQЧтениеEigen Portfolio construction via PCAЛабораторнаяEigen Portfolio construction via PCAПрограммирование
03Data Visualization & Clustering12 материалов

Unsupervised Learning

UL. Clustering AlgorithmsВидеоUL. K-clusteringВидеоUL. K-means Neural AlgorithmВидеоUL. Hierarchical Clustering AlgorithmsВидеоUL. Clustering and Estimation of Equity Correlation MatrixВидеоUL. Minimum Spanning Trees, Kruskal AlgorithmВидеоUL. Probabilistic ClusteringВидеоC. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 9ЧтениеG. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)Чтение

Module 3 Assessment

Jupyter Notebook FAQЧтениеData visualization with t-SNEЛабораторнаяData Visualization with t-SNEПрограммирование
04Sequence Modeling and Reinforcement Learning16 материалов

Sequence Modeling

SM. Latent VariablesВидеоSequence ModelingВидеоSM. Latent Variables for SequencesВидеоSM. State-Space ModelsВидеоSM. Hidden Markov ModelsВидеоNeural Architecture for Sequential DataВидеоC. Bishop, “Pattern Recognition and Machine Learning”, Chapter 13Чтение

Reinforcement Learning

RL. IntroductionВидеоRL. Core IdeasВидеоMarkov Decision Process and RLВидеоRL. Bellman EquationВидеоRL and Inverse Reinforcement LearningВидеоS. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 13Чтение

Course Project

Jupyter Notebook FAQЧтениеAbsorption Ratio via PCAЛабораторнаяAbsorption Ratio via PCAПрограммирование