1. Regression & Classification
- Linear
- Multivariate Linear
- Logistic
- Softmax
- Vectorization
- Gradient Calculation
- Stochastic Gradient Descent (SGD)
- Optimizers and Objectives
2. Regularization
- Ridge regression
3. Clustering
k – Means- EM Algorithms
4. Unsupervised Learning
- Autoencoders
- PCA Whitening
- sparse coding
5. Neural Network
- Perceptrons
- Backpropagation
- Restricted Boltzmann Machines
- Learning Vector Quantization
6. Deep Learning
- Stacked Autoencoders
- Convolution Neural Networks (Feature Extraction, Pooling)
- Deep Boltzmann Machines
- Deep Belief Networks
7. Decision Trees
ID3- C4.5
- CART (Classification and regression tree)
- Random Forests
8. Bayesian
Naïve Bayes- Gaussian Naïve Bayes
- Bayesian Networks
- Conditional Random Fields
- Hidden Markov Models
9. Others
- Support Vector Machines
- Evolutionary Methods
- Reinforcement Learning
- Conditional Random Fields
10. Dimensionality Reduction
- PCA
11. Ensemble Methods
- Boosting
- Bagging
- Adaboost
Comments
comments