With Python, TensorFlow and a plethora of other open source tools, anyone with a computer can run machine learning algorithms in a jiffy! However, without an understanding of which algorithms to choose and when to apply a technique, most machine learning efforts turn into trial and error experiments with conclusions like "The algorithms don't work" or "Perhaps we should get more data".

In this workshop, we will focus on the key tenets of machine learning algorithms and how to choose an algorithm for a problem. Rather than just showing how to run experiments in Python or TensorFlow, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

What you will learn

  • How do you differentiate Clustering, Classification and Prediction algorithms?
  • What are the key steps in running a machine learning algorithm?
  • How do you choose an algorithm for a specific goal?
  • Where does exploratory data analysis and feature engineering fit into the picture?
  • Once you run an algorithm, how do you evaluate the performance of an algorithm?
  • Role of Spark and Deep Learning techniques in Machine Learning
  • Practical Case studies with fully functional code
Eventbrite - Machine Learning for Finance: Boston and Online

Course summary


Day 1

On day one, we will review the core techniques in Machine Learning. Through examples we will understand the different machine learning techniques and review evaluation criteria

What you will learn

  • Machine Learning: An intuitive foundation
  • The Machine Learning pipeline
  • Supervised Learning: Classification and Prediction
  • Machine learning methods: Regression, KNN, Random Forests, Neural Networks
  • Evaluating performance
  • Case study 1: Predicting interest rates in Freddie Mac mortgage data
  • Case study 2: To give a loan or not using Lending club data


Day 2

On day two, we will discuss unsupervised learning techniques and discuss the role of Big Data and Deep Learning techniques for large-scale machine learning. We will also discuss best practices in scaling and using anomaly detection techniques.

What you will learn

  • Unsupervised learning: Clustering
  • Working with rare-class problems and Anomaly Detection
  • Machine Learning with Neural Networks: A deep dive
  • Deep Learning techniques
  • Wrap up and Best practices in Machine Learning
  • Case study 3: Using K-means for automatic clustering of stocks
  • Case study 4: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow

Sample Content from a prior workshop

Past QuantUniversity Workshop Attendees

Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..

Recent Courses

Offerings in 2018

Chicago and OnlineMay 30th and 31st

Deep Learning Workshop

QuantUniversity's Deep Learning Workshop provides the foundation to understand the core techniques in Deep Learning.

This is a hands-on course with examples in Python, Keras, TensorFlow and Spark

This workshop will be delivered in Chicago and Online by Dr.Victor Shnayder and Sri Krishnamurthy

Boston & OnlineJune 8th, 9th

Machine Learning Workshop

QuantUniversity's 2-day Machine Learning Workshop provides the core Data science and machine learning techniques and applications in finance.We will also discuss the role of Big data and Deep Learning

This is a hands-on course with examples in R, Python and Spark

This workshop will be delivered in Boston and online by Sri Krishnamurthy

Boston & OnlineJune 19th,20th

Anomaly Detection Workshop

QuantUniversity's 2-day Anomaly Detection Workshop provides the core techniques and best practices in Anomaly Detection and Outlier Analysis with cross-sectional and time series data.

This is a hands-on course with examples in R, Python and Spark

This workshop will be delivered in Boston and Online by Sri Krishnamurthy

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