Dive into a comprehensive 21-hour lecture series on machine learning, developed by the Amsterdam Machine Learning Lab at the University of Amsterdam. Follow the Pattern Recognition and Machine Learning book by Bishop as you explore fundamental concepts and advanced techniques. Begin with an introduction to machine learning types and probability theory before progressing to topics such as linear regression, model selection, neural networks, and unsupervised learning. Master key algorithms including stochastic gradient descent, logistic regression, and support vector machines. Delve into clustering methods, principal component analysis, and Gaussian processes. Conclude with an examination of model combination methods, bootstrapping, and decision trees. Gain a solid foundation in machine learning theory and practice through this comprehensive course taught by dr.ir. Erik Bekkers.
Overview
Syllabus
1.2 What Is Machine Learning (UvA - Machine Learning 1 - 2020).
1.3 Types Of Machine Learning (UvA - Machine Learning 1 - 2020).
1.4 Probability Theory Bayes (UvA - Machine Learning 1 - 2020).
1.5 Probability Theory: Example (UvA - Machine Learning 1 - 2020).
2.1 Expectation Variance (UvA - Machine Learning 1 - 2020).
2.2 Gaussian (UvA - Machine Learning 1 - 2020).
2.3 Maximum Likelihood (UvA - Machine Learning 1 - 2020).
2.4 Maximum Likelihood: Example (UvA - Machine Learning 1 - 2020).
2.5 Maximum A Posteriori (UvA - Machine Learning 1 - 2020).
2.6 Bayesian Prediction (UvA - Machine Learning 1 - 2020).
3.1 Linear Regression With Basis Functions (UvA - Machine Learning 1 - 2020).
3.2 Linear Regression Via Maximum Likelihood (UvA - Machine Learning 1 - 2020).
3.3 Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020).
3.4 Underfitting Overfitting (UvA - Machine Learning 1 - 2020).
3.5 Regularized Least Squares (UvA - Machine Learning 1 - 2020).
4.1 Model Selection (UvA - Machine Learning 1 - 2020).
4.2 Bias Variance Decomposition (UvA - Machine Learning 1 - 2020).
4.3 Gaussian Posteriors (UvA - Machine Learning 1 - 2020).
4.4 Sequential Bayesian Learning (UvA - Machine Learning 1 - 2020).
4.5 Bayesian Predictive Distributions (UvA - Machine Learning 1 - 2020).
5.1 Equivalent Kernel (UvA - Machine Learning 1 - 2020).
5.2 Bayesian Model Comparison (UvA - Machine Learning 1 - 2020).
5.3 Model Evidence Approximation and Empirical Bayes (UvA - Machine Learning 1 - 2020).
5.4 Classification With Decision Regions (UvA - Machine Learning 1 - 2020).
5.5 Decision Theory (UvA - Machine Learning 1 - 2020).
5.6 Probabilistic Generative Models (UvA - Machine Learning 1 - 2020).
6.1 Probabilistic Generative Modeling: Maximum Likelihood (UvA - Machine Learning 1 - 2020).
6.2 Probabilistic Generative Modeling: Discrete Data (UvA - Machine Learning 1 - 2020).
6.3 Discriminant Functions (UvA - Machine Learning 1 - 2020).
6.4 Discriminant Functions: Least Squares Regression (UvA - Machine Learning 1 - 2020).
6.5 Discriminant Functions: The Perceptron (UvA - Machine Learning 1 - 2020).
7.1 Classification With Basis Functions (UvA - Machine Learning 1 - 2020).
7.2 Probabilistic Discriminative Models: Logistic Regression (UvA - Machine Learning 1 - 2020).
7.3 Logistic Regression: Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020).
7.4 Logistic Regression: Newton Raphson (UvA - Machine Learning 1 - 2020).
8.1 Neural Networks (UvA - Machine Learning 1 - 2020).
8.2 Neural Networks: Universal Approximation Theorem (UvA - Machine Learning 1 - 2020).
8.3 Neural Networks: Losses (UvA - Machine Learning 1 - 2020).
8.4 Neural Networks: Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020).
8.5 Neural Networks: Backpropagation (UvA - Machine Learning 1 - 2020).
9.1 Unsupervised Learning: Latent Variable Models (UvA - Machine Learning 1 - 2020).
9.2 K-Means Clustering (UvA - Machine Learning 1 - 2020).
9.3 Intermezzo: Lagrange Multipliers (UvA - Machine Learning 1 - 2020).
9.4 Gaussian Mixture Models And Expectation Maximization (UvA - Machine Learning 1 - 2020).
10.1 Principal Component Analysis: Maximum Variance (UvA - Machine Learning 1 - 2020).
10.2 Principal Component Analysis: Minimal Reconstruction Error (UvA - Machine Learning 1 - 2020).
10.3 Probabilistic Principal Component Analysis (UvA - Machine Learning 1 - 2020).
10.4 Non-Linear Principal Component Analysis (UvA - Machine Learning 1 - 2020).
11.1 Kernelizing Linear Models (UvA - Machine Learning 1 - 2020).
11.2 The Kernel Trick (UvA - Machine Learning 1 - 2020).
11.3 Support Vector Machines: Maximum Margin Classifiers (UvA - Machine Learning 1 - 2020).
11.4 Intermezzo: Inequality Constraint Optimization (UvA - Machine Learning 1 - 2020).
11.5 Support Vector Machines: Kernel SVM (UvA - Machine Learning 1 - 2020).
11.6 Support Vector Machines: Soft-Margin Classifiers (UvA - Machine Learning 1 - 2020).
12.1 Some Properties Of Gaussian Distributions (UvA - Machine Learning 1 - 2020).
12.2 Kernelizing Bayesian Regression (UvA - Machine Learning 1 - 2020).
12.3 Gaussian Processes (UvA - Machine Learning 1 - 2020).
12.4 Gaussian Processes With An Exponential Kernel (UvA - Machine Learning 1 - 2020).
12.5 Gaussian Processes: Regression (UvA - Machine Learning 1 - 2020).
13.1 Model Combination Methods Vs Bayesian Model Averaging (UvA - Machine Learning 1 - 2020).
13.2 Bootstrapping And Feature Bagging (UvA - Machine Learning 1 - 2020).
13.3 Boosting (UvA - Machine Learning 1 - 2020).
13.4 Decision Trees And Random Forests (UvA - Machine Learning 1 - 2020).
Taught by
Erik Bekkers
