Mathematics & Statistics of Machine Learning & Data Science
Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning
What you’ll learn
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Mathematics and Statistics behind Machine Learning
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Mathematics and Statistics behind Neural Networks
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Mathematics and Statistics behind Deep Learning
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Probably Approximately Correct (PAC) Learning
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Vapnik-Chervonenkis (VC) Dimension
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Bayesian Decision Theory
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Parametric Methods
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Bernoulli Density
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Tuning Model Complexity
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Gaussian (Normal) Density
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Multivariate Methods
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Multivariate Normal Distribution
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Tuning Complexity
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Dimensionality Reduction
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Linear Discriminant Analysis
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Clustering
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Expectation-Maximization Algorithm
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Supervised Learning after Clustering
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k-Means Clustering
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Nonparametric Density Estimation
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Kernel Estimator
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k-Nearest Neighbor Estimator
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Condensed Nearest Neighbor
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Pruning
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Multivariate Trees
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Learning Vector Quantization
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v-SVM
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Multiclass Kernel Machines
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Model Selection in HMM
Requirements
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Just some high-school math and statistics (optional)
Who this course is for:
- People who want to start their career in Machine Learning
- People who want to learn Machine Learning
- People who want to learn Deep Learning
