Interactive demonstrations for linear and Gaussian process regressions
Here’s a cool interactive demo of linear regression where you can grab the data points, move them around, and see the fitted regression line changing. And something similar for Gaussian process regression, where you can add data points, play with the hyperparameters, and then see the inference for the curve.
7 common mistakes when doing Machine Learning
1. Take default loss function for granted
2. Use plain linear models for non-linear interaction
3. Forget about outliers
4. Use high variance model when n<<p
5. L1/L2/… regularization without standardization
6. Use linear model without considering multi-collinear predictors
7. Interpreting absolute value of coefficients from linear or logistic regression as feature importance
All Machine Learning Models Have Flaws
Here is a summary what is wrong with various frameworks for learning. To avoid being entirely negative, I added a column about what’s right as well.
Machine Learning for Programming
Peter Norvig keynotes on using machine learning techniques to solve more general software problems, helping both the advanced programmer and the novice one.
Some Intuition About the Theory of Statistical Learning
While I was working on the Theory of Statistical Learning, and the concept of consistency, I found the following popular graph. I was wondering if it was possible to generate such a graph, with some data, and some statistical model. And indeed, it is rather simple, and it gives nice intuition about possible interpretations.
The Awesome Ways Big Data Is Used Today To Change Our World
1. Understanding and Targeting Customers
2. Understanding and Optimizing Business Processes
3. Personal Quantification and Performance Optimization
4. Improving Healthcare and Public Health
5. Improving Sports Performance
6. Improving Science and Research
7. Optimizing Machine and Device Performance
8. Improving Security and Law Enforcement.
9. Improving and Optimizing Cities and Countries
10. Financial Trading