AIFI Interactive Workshop - Monday March 18, 2019

10am - 6pm

Metropolitan Pavilion – 2nd Floor
123 West 18th Street
New York
NY 10011

As many of the best minds in AI are in NYC for the main conference, we have taken the opportunity to put together a unique workshop day which is separate from the main conference and is aimed at analysts, quants and researchers from asset managers who want to learn new AI coding methods and the latest theory around applications for ML /DL / NN.

At this in-depth, “roll up your sleeves” session, you will receive expert, practical, hands-on tuition on the very latest tools and techniques you can utilize every day at work.
These sessions may not be suitable for beginners. Knowledge of programming and some understanding of deep learning is a necessity, and a list of requirements will be provided for each session, allowing you to ensure your suitability, and to download any necessary software in advance. You are a data scientist, software developer, data engineer, or financial data analyst who wants to use the very latest tools, technology and techniques to: analyze data sets; optimize portfolio risk models; refine trading strategies & back test. By attending this workshop, you will stay at the cutting-edge of your field. A great opportunity for personal development,
as well as ensuring that your firm keeps its edge.

Details on the workshop are listed below, please note that attendees will be required to bring their own laptops and to download application specific programs in advance to allow, for example, real ML queries to be created using real data on the latest cloud based GPU processors.

The workshop and conference are available to book separately. The workshop includes lunch and refreshments.

(Full day)

Finance Practitioners and Machine Learners will learn ML techniques in Finance and Implementation of ML projects in Finance. We will cover the most relevant ML and AI Algorithms. An excellent blend of mathematics, financial intuition and Python to learn Machine and Artificial Intelligence in Finance. 

Quantitative Finance 
- Review quantitative finance 
- Alternative data 
Machine Learning Modelling 
- Mathematics of machine learning 
- Machine learning modelling framework
Supervised Learning: Classification
- Logistic regression and Softmax regression 
- SVM’s and CART’s 
- Boosting and bagging: Random facts
- AdaBoost + XG Boost
Supervised Learning: Regression 
- Modern linear regression 
- Non-Linear regression
- Neural networks
- Deep neural networks
Supervised Learning: Deep Learning 
- Mathematics of deep learning 
- Deep learning architectures
Reinforcement Learning Natural Language Processing 
- Sentiment analysis – NLTK
Python and Exercises