MachineLearning

This Machine Learning folder is a collection of notes, lecture slides, and relevant concept check questions for my learning of Machine Learning. The folder is organized by topic as shown below.

Contents:

1. Hypothesis Space and Statistical Learning
2. Gradients
   1. Gradient Descent
   2. SGD
   3. Subgradient Descent
3. Regularizations
   1. L1
   2. L2
   3. Elastic Net
4. Loss Functions
5. Support Vector Machine
   1. SVM
   2. SVM and Complementary Slackness
   3. Geometrics Derivation of SVMs
   4. Uniqueness of SVM Solution
   5. Lagrangian Duality and Convex Optimization
6. Kernel Methods
   1. Kernel Methods
   2. Representer Theorem
7. Conditional Probability Models
   1. Maximum Likelihood
   2. Multivariate Gaussian
8. Bayesian Methods and Regression
   1. Bayesian Methods
   2. Bayesian Conditional Models
   3. Bayesian Linear Regression
9. Multiclass
10. Trees
    1. Bootstrap
    2. Bagging and Random Forest
    3. Boosting
       1. AdaBoost
       2. Forward Stage-wise Additive Modeling
       3. Gradient Boosting
11. Distribution Modeling with Generalized Linear Model (GLM) and Gradient Boosting Machine (GBM) Approaches
    1. Exponential Distribution
    2. Poisson Distribution
12. Neural Network
    1. Back Propagation
13. Gaussian Mixture Model
14. Expectation Maximization Algorithm
    1. Expectation
    2. Maximization