
Course Syllabus at a glance
Artificial Intelligence in Finance – Dr. Miquel Noguer
Big Data in Finance Modeling I – Dr. Miquel Noguer
a. Supervised learning
b. Unsupervised
c. NLP and RL
Quantitative Finance – Petter Kolm
RL in Finance – William Kelly
Python and coding – Primer – Nicole Koenigstein
a. Python basics
b. Supervised Learning Algorithms
Supervised Learning – Dr. Noguer / Vanegas
a. Supervised Learning Framework
b. NN, SVM, CART, XgBoost
Machine Learning and Market Microstructure – Sasha Stoikov
Unsupervised Learning – Stefan Jansen
a. Unsupervised Learning Rationale
b. Clustering
c. Auto-Encoders
d. Dimension reduction: PCA
Python and coding – Machine Learning – Nicole Koenigstein
a. Python Sci kit Learn
Generative Models – David Pacheco
RL Basics – David Pacheco
Ml In Finance – Dhagash Mehta
NLP Basics – Asier Gutierrez
ML and Factor Models – Vincent Zoonekynd
Python and coding – Machine Learning – Nicole Königstein
Deep Learning Applications – Stefan Jansen
a. DL computational aspects
b. Factor Models
RL in Finance – Gordon Ritter
Deep Learning –Dr. Matthew Dixon
b. Neural Networks Architectures
II. Recurrent Neural Networks
III. Long Short Term Memory Networks
IV. Convolutional Neural Networks
V. Generative Adversarial Networks
Reinforcement Learning – Dr. Matthew Dixon
a. Reinforcement Learning in Finance
b. RL in Dynamic Rep and Hedging
c. Markov Decision processes
d. Taxonomy of RL
e. Tabular Learning and Bellman Equation
f. Deep Q-Networks
Machine Learning in Finance – Nicholas Westray
Reinforcement Learning II – Amine Aboussalah
a. Reinforcement Learning Definitions
b. Trading Example
Mathworks – Marshall Alphonso
Natural Language Processing – Dr. Miquel Noguer
a. NLP Pipeline
b. Text Pre-Processing
c. Word Tokenization
d. Deep Learning NLP
NLP Deep Learning – Tinghao Li
a. Transformers
b. BERT
NLP applied to ESG and SDG – Richard Rothenberg
RL in Finance – Igor halperin