Об этом 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.
Для кого этот курс: 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.
Преподаватели: Igor Halperin
| Основные сведения | |
| Уровень | Учащийся среднего уровня |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Видео: Introduction to Fundamentals of Machine Learning in Finance
- Видео: Support Vector Machines, Part 1
- Видео: Support Vector Machines, Part 2
- Видео: SVM. The Kernel Trick
- Видео: Example: SVM for Prediction of Credit Spreads
- Reading: A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 2004
- Видео: Tree Methods. CART Trees
- Видео: Tree Methods: Random Forests
- Видео: Tree Methods: Boosting
- Reading: A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 7
- Reading: K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.4
- Reading: Jupyter Notebook FAQ
- Notebook: Random Forests And Decision Trees
- Видео: PCA for Stock Returns, Part 1
- Видео: PCA for Stock Returns, Part 2
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.1
- Видео: Dimension Reduction with PCA
- Видео: Dimension Reduction with tSNE
- Видео: Dimension Reduction with Autoencoders
- Reading: A. Geron, “Hands-On ML”, Chapters 8 & 15
- Reading: Jupyter Notebook FAQ
- Notebook: Eigen Portfolio construction via PCA
- Видео: UL. K-clustering
- Видео: UL. K-means Neural Algorithm
- Видео: UL. Hierarchical Clustering Algorithms
- Видео: UL. Clustering and Estimation of Equity Correlation Matrix
- Видео: UL. Minimum Spanning Trees, Kruskal Algorithm
- Видео: UL. Probabilistic Clustering
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 9
- Reading: G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)
- Reading: Jupyter Notebook FAQ
- Notebook: Data visualization with t-SNE
- Видео: Sequence Modeling
- Видео: SM. Latent Variables for Sequences
- Видео: SM. State-Space Models
- Видео: SM. Hidden Markov Models
- Видео: Neural Architecture for Sequential Data
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 13
- Видео: RL. Introduction
- Видео: RL. Core Ideas
- Видео: Markov Decision Process and RL
- Видео: RL. Bellman Equation
- Видео: RL and Inverse Reinforcement Learning
- Reading: S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 13
- Reading: Jupyter Notebook FAQ
- Notebook: Absorption Ratio via PCA
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The lectures were truly outstanding, the best overview on different methods in machine learning I have seen so far. The problem sets were also interesting, informative and introduced several useful api from sklearn, tensorflow. With a little work these problem sets could (and probably should) be improved to match the quality of the lectures. For example adding more clarifications in the homework notebooks would be very helpful. Having said this, I think this is an excellent course, and highly recommend it.
Good material..The course would improve a lot if there were clear explanations for the goals of the assignments and the plan for the assignment.. The codes for the assignment should be fully debugged..
BK
Add some hints in the notebooks, it was very hard to understand some parts