UNIT 1:
Basic Concepts in Machine Learning – Machine Learning Process
Testing Machine Learning Algorithm
Types of Machine Learning – Supervised Learning – Unsupervised Learning
A Brief Review of Probability Theory
UNIT 2:
Probabilistic Generative Models – Probabilistic Discriminative Models
Linear Models for Regression – Linear Basis Function Models
– Linear Models for Classification – Discriminate Functions
The Bias-Variance Tradeoff
Bayesian Linear Regression – Common Regression Algorithms
Common Classification Algorithms – k-Nearest Neighbors
Support Vector Machines – Case Studies.
Decision Trees – Random Forest model
UNIT 3:
Mixture Models and EM – K-Means Clustering
Mixture Models and EM – K-Means Clustering
Dirichlet Process Mixture Models – Spectral Clustering
The Curse of Dimensionality – Dimensionality Reduction
Principal Component Analysis
Latent Variable Models(LVM)
Latent Dirichlet Allocation (LDA) – Case Studies.
Dirichlet Process Mixture Models – Spectral Clustering
Principal Component Analysis
Latent Dirichlet Allocation (LDA) – Case Studies.
UNIT 4:
Bayesian linear regression
