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.
Machine Learning and Reinforcement Learning in Finance の専門講座New York Universityこのコースについて
19,635 最近の表示
受講生の就業成果
20%
コース終了後に新しいキャリアをスタートした
18%
コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約18時間で修了
英語
字幕：英語
受講生の就業成果
20%
コース終了後に新しいキャリアをスタートした
18%
コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約18時間で修了
英語
字幕：英語
提供：
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
シラバス - 本コースの学習内容
5時間で修了
Fundamentals of Supervised Learning in Finance
5時間で修了
9件のビデオ （合計71分）, 4 readings, 1 quiz
9件のビデオ
Introduction to Fundamentals of Machine Learning in Finance4 分
Support Vector Machines, Part 18 分
Support Vector Machines, Part 27 分
SVM. The Kernel Trick8 分
Example: SVM for Prediction of Credit Spreads9 分
Tree Methods. CART Trees9 分
Tree Methods: Random Forests8 分
Tree Methods: Boosting9 分
4件の学習用教材
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415 分
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730 分
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415 分
Jupyter Notebook FAQ10 分
4時間で修了
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
4時間で修了
6件のビデオ （合計54分）, 3 readings, 1 quiz
6件のビデオ
PCA for Stock Returns, Part 14 分
PCA for Stock Returns, Part 29 分
Dimension Reduction with PCA9 分
Dimension Reduction with tSNE11 分
Dimension Reduction with Autoencoders9 分
3件の学習用教材
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115 分
A. Geron, “Hands-On ML”, Chapters 8 & 1530 分
Jupyter Notebook FAQ10 分
4時間で修了
Data Visualization & Clustering
4時間で修了
7件のビデオ （合計50分）, 3 readings, 1 quiz
7件のビデオ
UL. K-clustering8 分
UL. K-means Neural Algorithm7 分
UL. Hierarchical Clustering Algorithms10 分
UL. Clustering and Estimation of Equity Correlation Matrix5 分
UL. Minimum Spanning Trees, Kruskal Algorithm6 分
UL. Probabilistic Clustering6 分
3件の学習用教材
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930 分
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15 分
Jupyter Notebook FAQ10 分
5時間で修了
Sequence Modeling and Reinforcement Learning
5時間で修了
11件のビデオ （合計101分）, 3 readings, 1 quiz
11件のビデオ
Sequence Modeling10 分
SM. Latent Variables for Sequences8 分
SM. State-Space Models9 分
SM. Hidden Markov Models9 分
Neural Architecture for Sequential Data12 分
RL. Introduction8 分
RL. Core Ideas7 分
Markov Decision Process and RL8 分
RL. Bellman Equation6 分
RL and Inverse Reinforcement Learning11 分
3件の学習用教材
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310 分
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315 分
Jupyter Notebook FAQ10 分
レビュー
FUNDAMENTALS OF MACHINE LEARNING IN FINANCE からの人気レビュー
JJにより2018年12月24日
So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.
ATにより2019年8月9日
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
ATにより2019年9月2日
Great course which covers both theories as well as practical skills in the real implementations in the financial world.
AMにより2020年1月1日
Excellent course to get ML algorithms for profit maximization approach
Machine Learning and Reinforcement Learning in Finance専門講座について
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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