Machine Learning Algorithms
Supervised Learning
Linear Regression (Hypothesis, Uni-variable, Cost-function, Gradient Descent, Multivariable, Feature Scaling, Mean normalization)
Polynomial Regression
Normal Equation (Algorithm, Comparison with Gradient Descent, Case with Non-invertability matrix )
Logistic Regression for Classification (Hypothesis, Non-linear decision boundaries, Cost-function, Simplified cost-function and Gradient Descent)
Advanced Optimization
Advanced Algorithms (Conjugate Gradient, BFGS, L-BFGS)
Multi-class classification: One-vs-all
Regularization: Over-fitting problem, Cost-function, Linear Regression, Normal Equation, Logistic Regression, Advanced Algs)
Take away from AI-Class
Applications
Supervised Learning
OCCAM’s RAZOR
Spam Detection (with Naive Bayes)
Maximum Likelihood
Laplace Smoothing
Advanced SPAM filtering
Handwriting classification
Overfitting prevetion
Linear Regression
Regularization
Perceptron Algorithm
Maximum Margin Algorithms (SVM, Boosting)
K-nearest neighbors (Algorithm, Problems)
Unsupervised Learning
Density estimation, Dimensionality Reduction, Blind sequence separation, Factor Analysis
Clustering
k-means (algorithm, problems)
Expectation minimization
Gaussians and Normal Distribution
Gaussian Learning
Algorithm
# of cluster calculation
Dimensionality Reduction
Spectral Clustering