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.
本课程是 Machine Learning and Reinforcement Learning in Finance 专项课程 专项课程的一部分
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New York University
New York University is a leading global institution for scholarship, teaching, and research. Based in New York City with campuses and sites in 14 additional major cities across the world, NYU embraces diversity among faculty, staff and students to ensure the highest caliber, most inclusive educational experience.
授课大纲 - 您将从这门课程中学到什么
Fundamentals of Supervised Learning in Finance
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
Data Visualization & Clustering
Sequence Modeling and Reinforcement Learning
审阅
- 5 stars45.51%
- 4 stars21.15%
- 3 stars14.74%
- 2 stars5.12%
- 1 star13.46%
来自FUNDAMENTALS OF MACHINE LEARNING IN FINANCE的热门评论
This is a great course, I strongly recommend. However, the assignments take a while to finish.
Great course which covers both theories as well as practical skills in the real implementations in the financial world.
Great class, but don't believe the programming assignment time estimates... takes way longer!
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
关于 Machine Learning and Reinforcement Learning in Finance 专项课程
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.

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