Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 

Short Course

Applied Machine Learning for Risk Management

The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision—Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Machine Learning technology.

This course on Machine Learning will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include linear regression, logistic regression, deep neural networks, clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, Credit Risk, Collections Management and Fraud Detection.

Instructors:

Dr. Alejandro Correa Bahnsen

Iván Torroledo

Requiriments

  • Python version 3.5;
  • Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
  • Scipy, additional libraries for scientific programming;
  • Matplotlib, excellent plotting and graphing libraries;
  • IPython, with the additional libraries required for the notebook interface.
  • Pandas, Python version of R dataframe
  • scikit-learn, Machine learning library!

A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.

GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials

Sessions

Session Notebook link Exercises
1 Introduction to Machine Learning
2 Introduction to Python for Data Analysis Python & Numpy & Pandas
3 Linear Regression Income Prediction Rent
4 Logistic Regression Credit Scoring
5 Data Preparation and Model Evaluation Credit Scoring V2
6 Unbalance Datasets Fraud Detection
7 Decision Trees Fraud Detection V2
8 Ensemble Methods - Bagging Fraud Detection V3
9 Statistical Inference
10 Cost-Sensitive Classification Credit Scoring V4
11 Model Deployment

About

Short Course - Applied Machine Learning for Risk Management

Resources

License

Releases

No releases published

Packages

No packages published