Machine Learning is a multidisciplinary field of research focusing on the mathematical foundations and practical systems that learn, reason and act. Machine learning conducts the analysis and modeling of large complex data. Machine learning makes extensive use of computational and statistical methods, and takes inspiration from biological neural systems.

Learning Process

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning

Theories and Algorithms of Machine Learning

• Bayesian Learning
• Online Learning
• Discriminative Training
• Multi-Task Learning
• Active Learning
• Information-Theoretic Learning
• Linear Regression
• Linear Classification
• Model Selection
• Data Clustering
• Regularization Theory
• Support Vector Machines
• Gaussian Process
• Kernel Methods
• Deep Learning
• Transfer Learning
• Learning to Rank
• Graphical Models
• Neural Networks
• Variational Bayesian Methods
• Dimensionality Reduction
• Sparse Approximation
• Compressive Sensing
• Non-Parametric Bayesian Methods
• Independent Component Analysis

Applications of Machine Learning

• Speech Recognition/Enhancement/Synthesis
           Acoustic Models
           Language Models
• Face Recognition
• Information Retrieval
           Topic Models
           Collaborative Filtering
           Text Categorization
• Blind Source Separation
• Computer Vision
• Optical Character Recognition
• Robotics
• Bioinformatics
• Medical Images and Signals
• Data Mining
• Web and Social Data Modeling