Fundamentals of Big Data Analytics
Lecture Contents
- Matrix Algebra and Extremals of Matrix Functions
- Multivariate Analysis and Random Vectors
- Dimensionality Reduction Methods
- Principal Component Analysis
- Multidimensional Scaling
- IsoMap
- Diffusion Map
- Classification and Cluster Analysis
- Linear Discriminant Analysis
- Maximum A-posteriori Probability (MAP) Classifier
- Maximum Likelihood (ML) Classifier
- k-Means
- Spectral Clustering
- Hierarchical Clustering
- Support-Vector Machines
- Convex Optimization Theory
- Support-Vector Machine (SVM) Algorithm
- Sequential Minimal Optimization (SMO) Algorithm
- Kernel SVM
- Regression
- linear
- logistic
- perceptron
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