Mathematics & Statistics of Machine Learning & Data Science

Mathematics & Statistics of Machine Learning & Data Science

What you’ll learn

• Mathematics and Statistics behind Machine Learning
• Mathematics and Statistics behind Neural Networks
• Mathematics and Statistics behind Deep Learning
• Probably Approximately Correct (PAC) Learning
• Vapnik-Chervonenkis (VC) Dimension
• Bayesian Decision Theory
• Parametric Methods
• Bernoulli Density
• Tuning Model Complexity
• Gaussian (Normal) Density
• Multivariate Methods
• Multivariate Normal Distribution
• Tuning Complexity
• Dimensionality Reduction
• Linear Discriminant Analysis
• Clustering
• Expectation-Maximization Algorithm
• Supervised Learning after Clustering
• k-Means Clustering
• Nonparametric Density Estimation
• Kernel Estimator
• k-Nearest Neighbor Estimator
• Condensed Nearest Neighbor
• Pruning
• Multivariate Trees
• Learning Vector Quantization
• v-SVM
• Multiclass Kernel Machines
• Model Selection in HMM