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다음 특화 과정의 4개 강좌 중 3번째 강좌:
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중급 단계
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영어
자막: 영어

배울 내용

  • Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

  • Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes

  • Utilize powerful Python libraries to implement machine learning algorithms in case studies

  • Learn about factor models and regime switching models and their use in investment management

귀하가 습득할 기술

Programming skillsManaging your own personal invetsmentsInvestment management knowledgeComputer ScienceExpertise in data science
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 15시간 필요
영어
자막: 영어

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EDHEC 경영대학원 로고

EDHEC 경영대학원

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 2시간 필요

Introducing the fundamentals of machine learning

완료하는 데 2시간 필요
8개 동영상 (총 59분), 4 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Introduction to machine-learning7m
Financial applications7m
Supervised learning7m
First algorithms7m
Highlights of best practice6m
Unsupervised learning7m
Challenges ahead10m
4개의 읽기 자료
Requirements2m
Material at your disposal2m
Machine Learning for Investment Decisions: A Brief Guided Tour10m
References for module 1"Introducing the fundamentals of machine learning"10m
1개 연습문제
Module 1Graded Quiz30m
2

2

완료하는 데 4시간 필요

Machine learning techniques for robust estimation of factor models

완료하는 데 4시간 필요
8개 동영상 (총 80분), 2 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Introducing Factor Models7m
Typology of factor models9m
Using factor models in portfolio construction and analysis10m
Penalty methods9m
Setting factor loadings and examples7m
Shrinkage concepts7m
Lab session - Jupiter notebook on Factor Models20m
2개의 읽기 자료
References for module 2"Machine learning techniques for robust estimation of factor models"10m
Information on Jupyter notebook - Factor models10m
1개 연습문제
Module 2 Graded Quiz1시간
3

3

완료하는 데 2시간 필요

Machine learning techniques for efficient portfolio diversification

완료하는 데 2시간 필요
7개 동영상 (총 59분), 2 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
2개의 읽기 자료
References for the module "Machine learning techniques for efficient portfolio diversification"10m
Reference for the module "Selecting a portfolio of assets"10m
1개 연습문제
Module 3 Graded Quiz45m
4

4

완료하는 데 3시간 필요

Machine learning techniques for regime analysis

완료하는 데 3시간 필요
7개 동영상 (총 65분), 4 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Portfolio Decisions with Time-Varying Market Conditions10m
Trend filtering6m
A scenario based portfolio model8m
A two regime portfolio example7m
A multi regime model for a University Endowment9m
Lab session- Jupyter notebook on regime-based investment model15m
4개의 읽기 자료
Information on the "trend filtering" video2m
Information on "scenario based portfolio model" video2m
References for the module "Machine learning techniques for regime analysis"10m
Information on Jupyter notebookon regime-based investment model10m
1개 연습문제
Module 4 Graded Quiz1시간

검토

PYTHON AND MACHINE LEARNING FOR ASSET MANAGEMENT 의 최상위 리뷰

모든 리뷰 보기

Investment Management with Python and Machine Learning 특화 과정 정보

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions....
Investment Management with Python and Machine Learning

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