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Practical Machine Learning

Instructor: Alejandro Correa Bahnsen

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 algorithnms and interweaves them with computer systems. It underpins many modern technologies, such as speech recognition, internet search, bioinformatics, 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 machine learning, python data analysis, deep learning frameworks, natural language processing models and recurrent models. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, image analysis, image captioning, natural language pocessing, sentiment detection, among others.


  • 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
  • Seaborn, used mainly for plot styling
  • 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


  • 30% Exercises
  • 50% Projects
  • 20% Final Project


Supervised Machine Learning

Date Session Notebooks/Presentations Exercises
January 21st Introduction to python and ML
January 28th Linear Models
February 4th SVM & Decision Trees
February 11th Machine Learning as a Service
February 18th Ensembles
February 25th Random Forest
March 4th Feature Engineering
March 11th Project Presentations
March 18th Unbalanced Learning

Natural Language Processing

Date Session Notebooks/Presentations Exercises
April 1st Natural Language Processing
April 8th Sentiment Analysis
April 22nd Project Presentations

Advanced Topics in Machine Learning

Date Session Notebooks/Presentations Exercises
April 29th Introduction to Deep Learning
May 6th Introduction to Deep Learning II

Final Project Presentation


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