Machine Learning is awesome

Machine Learning Class

Machine Learning has always been a topic that attracted my interest. Currently I attend the “Machine Learning” class (@ml_class) offered by Prof. Andrew Ng from Stanford University. The class is awesome. The video lectures are made of small chunks explaining every topic in detail. A lot of topics were already covered:

  • Linear regression with one variable
  • Linear regression with multiple variable
  • One-vs-all Classification
  • Regularization
  • Backpropagation Algorithm
  • Neural Networks
  • Practical advise for applying learning algorithms
  • How to develop and debug learning algorithms
  • Feature and model design, setting up experiments

Other interesting topics are following. In parallel to the lectures there are homework programming exercises that have to be solved. To date programming assignments covering topics as:

  • Linear regression
  • Logistic regression
  • Multi-class classification and Neural Networks
  • Neural network learning
  • Regularized linear regression and bias-variance

In order to solve the exercises you have to understand the contents and have some programming experience in GNU Octave. It is also important to have basic understanding of Linear Algebra.

Machine Learning Contests

In order to apply machine learning on real world problems you can enter a machine learning contest. There are various contests out there, some of which are

I will keep you updated.

Enhanced by Zemanta

[video] – The Filter Bubble – From Human to Algorithmic Gatekeepers

Web personalization and personalized recommendations are recently gaining more and more interest. Companies like Amazon, Google, Netflix, The New York Times, Facebook, Twitter, … already personalize their products in different ways. If you take Google’s search results as an example. Have you ever noticed that a friend of you gets different search results as you do for the same search query? If you never have noticed just try it out it’s really worth noting. Another example are Amazon’s product recommendations which are for example based on your purchases, your product ratings and so on.

Eli Pariser explains in the following TED Talk how “human information filters” get substituted by algorithmic ones, which means how recommendation engines filter information for you. Have a look at the video is is really worth watching:

Do you know other examples of web personalization or recommendations engines? Please leave me a comment at the end of this post.

Enhanced by Zemanta

[video] – How is Hadoop used at Twitter?

Image representing Hadoop as depicted in Crunc...
Image via CrunchBase

In the following video Dmitriy Ryaboy, a Twitter Analytics Engineer and a former Cloudera Intern, explains how Twitter uses Hadoop and Pig. Enjoy the video and have a good weekend!

[vimeo 11110059]

Enhanced by Zemanta

Video by Google on K-Means Clustering

Recently I was searching for good ressources explaining the k-means clustering algorithm. I found the following video from Google Research on YouTube. It explains the K-Means clustering algorithm quite well.