This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. 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.
이 강좌는 Machine Learning and Reinforcement Learning in Finance 전문 분야의 일부입니다.
Guided Tour of Machine Learning in Finance
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About this Course
최근 조회 38,362회
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Learner Career Outcomes
50%
이 강좌를 수료한 후 새로운 경력 시작하기
47%
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 4개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 21시간 필요
권장: 11 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 3시간 필요
Artificial Intelligence & Machine Learning
11개 동영상 (총 75분), 3 readings, 1 quiz
11개의 동영상
Specialization Objectives8m
Specialization Prerequisites7m
Artificial Intelligence and Machine Learning, Part I6m
Artificial Intelligence and Machine Learning, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part I5m
Machine Learning as a Foundation of Artificial Intelligence, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part III7m
Machine Learning in Finance vs Machine Learning in Tech, Part I6m
Machine Learning in Finance vs Machine Learning in Tech, Part II6m
Machine Learning in Finance vs Machine Learning in Tech, Part III8m
3개의 읽기 자료
The Business of Artificial Intelligence30m
How AI and Automation Will Shape Finance in the Future30m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m
1개 연습문제
Module 1 Quiz30m
주
2완료하는 데 6시간 필요
Mathematical Foundations of Machine Learning
6개 동영상 (총 45분), 3 readings, 2 quizzes
6개의 동영상
The No Free Lunch Theorem7m
Overfitting and Model Capacity8m
Linear Regression7m
Regularization, Validation Set, and Hyper-parameters10m
Overview of the Supervised Machine Learning in Finance3m
3개의 읽기 자료
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41h
Leo Breiman, “Statistical Modeling: The Two Cultures”1h
Jupyter Notebook FAQ10m
1개 연습문제
Module 2 Quiz15m
주
3완료하는 데 6시간 필요
Introduction to Supervised Learning
7개 동영상 (총 75분), 4 readings, 2 quizzes
7개의 동영상
A First Demo of TensorFlow11m
Linear Regression in TensorFlow10m
Neural Networks11m
Gradient Descent Optimization10m
Gradient Descent for Neural Networks12m
Stochastic Gradient Descent8m
4개의 읽기 자료
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1h
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15m
J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-4115m
Jupyter Notebook FAQ10m
1개 연습문제
Module 3 Quiz15m
주
4완료하는 데 10시간 필요
Supervised Learning in Finance
9개 동영상 (총 66분), 4 readings, 3 quizzes
9개의 동영상
Fundamental Analysis7m
Machine Learning as Model Estimation8m
Maximum Likelihood Estimation10m
Probabilistic Classification Models6m
Logistic Regression for Modeling Bank Failures, Part I8m
Logistic Regression for Modeling Bank Failures, Part II5m
Logistic Regression for Modeling Bank Failures, Part III8m
Supervised Learning: Conclusion2m
4개의 읽기 자료
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1h
Jupyter Notebook FAQ10m
Jupyter Notebook FAQ10m
1개 연습문제
Module 4 Quiz21m
3.8
133개의 리뷰Guided Tour of Machine Learning in Finance의 최상위 리뷰
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
뉴욕 대학교 공과 대학 정보
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Machine Learning and Reinforcement Learning in Finance 전문 분야 정보
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.
The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:
(1) mapping the problem on a general landscape of available ML methods,
(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and
(3) successfully implementing a solution, and assessing its performance.
The specialization 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.
The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.
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