About this Course

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Learner Career Outcomes

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Intermediate Level
Approx. 18 hours to complete
English

Instructor

Learner Career Outcomes

20%

started a new career after completing these courses

18%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 18 hours to complete
English

Offered by

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New York University

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Fundamentals of Supervised Learning in Finance

5 hours to complete
9 videos (Total 71 min), 4 readings, 1 quiz
9 videos
Introduction to Fundamentals of Machine Learning in Finance4m
Support Vector Machines, Part 18m
Support Vector Machines, Part 27m
SVM. The Kernel Trick8m
Example: SVM for Prediction of Credit Spreads9m
Tree Methods. CART Trees9m
Tree Methods: Random Forests8m
Tree Methods: Boosting9m
4 readings
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730m
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415m
Jupyter Notebook FAQ10m
Week
2

Week 2

4 hours to complete

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

4 hours to complete
6 videos (Total 54 min), 3 readings, 1 quiz
6 videos
PCA for Stock Returns, Part 14m
PCA for Stock Returns, Part 29m
Dimension Reduction with PCA9m
Dimension Reduction with tSNE11m
Dimension Reduction with Autoencoders9m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115m
A. Geron, “Hands-On ML”, Chapters 8 & 1530m
Jupyter Notebook FAQ10m
Week
3

Week 3

4 hours to complete

Data Visualization & Clustering

4 hours to complete
7 videos (Total 50 min), 3 readings, 1 quiz
7 videos
UL. K-clustering8m
UL. K-means Neural Algorithm7m
UL. Hierarchical Clustering Algorithms10m
UL. Clustering and Estimation of Equity Correlation Matrix5m
UL. Minimum Spanning Trees, Kruskal Algorithm6m
UL. Probabilistic Clustering6m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930m
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15m
Jupyter Notebook FAQ10m
Week
4

Week 4

5 hours to complete

Sequence Modeling and Reinforcement Learning

5 hours to complete
11 videos (Total 101 min), 3 readings, 1 quiz
11 videos
Sequence Modeling10m
SM. Latent Variables for Sequences8m
SM. State-Space Models9m
SM. Hidden Markov Models9m
Neural Architecture for Sequential Data12m
RL. Introduction8m
RL. Core Ideas7m
Markov Decision Process and RL8m
RL. Bellman Equation6m
RL and Inverse Reinforcement Learning11m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310m
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315m
Jupyter Notebook FAQ10m

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Machine Learning and Reinforcement Learning in Finance

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