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
- Tuned IT
- Kaggle
I will keep you updated.