All the algorithms in the project are from 'Pattern Recognition and machine learning' by Bishop
They are explained in my blog:
- Assessing the Accuracy of the Model
- Are the Parameters Calculated from Least Squared Error Correct?
- Assessing the Accuracy of the Model
- Estimating Multiple Linear Regress Coefficients
- Least Squares Estimation
- Maximum Likelihood Estimation
- Polynomial Regression and Features-Extension of Linear Regression
- From Linear Regression to Linear Classification
- An Introduction to Discriminant Functions
- Least Squares
- Fisher Linear Discriminant(LDA)
- An Introduction to Probabilistic Generative Models for Linear Classification
- Logistic Regression
- An Introduction to Mixture Models
- K-means Clustering
- Mixtures of Gaussians
- Maximum Likelihood of Gaussian Mixtures
- EM Algorithm
- An Introduction to Combining Models
- Bayesian Model Averaging(BMA) and Combining Models
- Committees
- Boosting and AdaBoost