Acerca de este curso: 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.
Para quién es esta clase: 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.
Enseñado por: Igor Halperin
| Información básica | |
| Nivel | Intermedio |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Vídeo: Introduction to Fundamentals of Machine Learning in Finance
- Vídeo: Support Vector Machines, Part 1
- Vídeo: Support Vector Machines, Part 2
- Vídeo: SVM. The Kernel Trick
- Vídeo: 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
- Vídeo: Tree Methods. CART Trees
- Vídeo: Tree Methods: Random Forests
- Vídeo: 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
- Vídeo: PCA for Stock Returns, Part 1
- Vídeo: PCA for Stock Returns, Part 2
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.1
- Vídeo: Dimension Reduction with PCA
- Vídeo: Dimension Reduction with tSNE
- Vídeo: Dimension Reduction with Autoencoders
- Reading: A. Geron, “Hands-On ML”, Chapters 8 & 15
- Reading: Jupyter Notebook FAQ
- Notebook: Eigen Portfolio construction via PCA
- Vídeo: UL. K-clustering
- Vídeo: UL. K-means Neural Algorithm
- Vídeo: UL. Hierarchical Clustering Algorithms
- Vídeo: UL. Clustering and Estimation of Equity Correlation Matrix
- Vídeo: UL. Minimum Spanning Trees, Kruskal Algorithm
- Vídeo: 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
- Vídeo: Sequence Modeling
- Vídeo: SM. Latent Variables for Sequences
- Vídeo: SM. State-Space Models
- Vídeo: SM. Hidden Markov Models
- Vídeo: Neural Architecture for Sequential Data
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 13
- Vídeo: RL. Introduction
- Vídeo: RL. Core Ideas
- Vídeo: Markov Decision Process and RL
- Vídeo: RL. Bellman Equation
- Vídeo: 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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| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
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