Statistical Machine Learning 2020. Part 1: Machine learning and inductive bias. Part 2: Warmup: The kNN Classifier. Part 3: Formal setup, risk, consistency. Part 4: Bayesian decision theory. Part 5: The Bayes classifier.
Overview
Syllabus
Introduction - Probabilistic and Statistical Machine Learning 2020.
Statistical Machine Learning Part 1 - Machine learning and inductive bias.
Statistical Machine Learning Part 2 - Warmup: The kNN Classifier.
Statistical Machine Learning Part 3 - Formal setup, risk, consistency.
Statistical Machine Learning Part 4 - Bayesian decision theory.
Statistical Machine Learning Part 5: The Bayes classifier.
Statistical Machine Learning Part 6 - Risk minimization, approximation and estimation error.
Statistical Machine Learning Part 7a - What is a convex optimization problem?.
Statistical Machine Learning Part 7 - Linear least squares.
Statistical Machine Learning Part 8 - Feature representation.
Statistical Machine Learning Part 9 - Ridge regression.
Statistical Machine Learning Part 10 - Lasso.
Statistical Machine Learning Part 11 - Cross validation.
Statistical Machine Learning Part 12 - Risk minimization vs. probabilistic approaches.
Statistical Machine Learning Part 13 - Linear discriminant analysis.
Statistical Machine Learning Part 14 - Logistic regression.
Statistical Machine Learning Part 15 - Convex optimization, Lagrangian, dual problem.
Statistical Machine Learning Part 16 - Support vector machines: hard and soft margin.
Statistical Machine Learning Part 17 - Support vector machines: the dual problem.
Statistical Machine Learning Part 18 - Kernels: definitions and examples.
Statistical Machine Learning Part 19 - The reproducing kernel Hilbert space.
Statistical Machine Learning Part 20 - Kernel SVMs.
Statistical Machine Learning Part 21 - Kernelizing least squares regression.
Statistical Machine Learning Part 22 - How to center and normalize in feature space.
Statistical Machine Learning Part 23a - Random forests: building the trees.
Statistical Machine Learning Part 23b - Random forests: building the forests.
Statistical Machine Learning Part 24 - Boosting.
Statistical Machine Learning Part 25 - Principle Component Analysis.
Statistical Machine Learning Part 26 - Kernel PCA.
Statistical Machine Learning Part 27 - Multidimensional scaling.
Statistical Machine Learning Part 28 - Random projections and the Theorem of Johnson-Lindenstrauss.
Statistical Machine Learning Part 29 - Neighborhood graphs.
Statistical Machine Learning Part 30 - Isomap.
Statistical Machine Learning Part 31 - t-SNE.
Statistical Machine Learning Part 32 - Introduction to clustering.
Statistical Machine Learning Part 33 - k-means clustering.
Statistical Machine Learning Part 34 - Linkage algorithms for hierarchical clustering.
Statistical Machine Learning Part 35 - Spectral graph theory.
Statistical Machine Learning Part 36 - Spectral clustering, unnormalized case.
Statistical Machine Learning Part 37 - Spectral clustering: normalized, regularized.
Statistical Machine Learning Part 38 - Statistical learning theory: Convergence and consistency.
Statistical Machine Learning Part 39 - Statistical learning theory: finite function classes.
Statistical Machine Learning Part 40 - Statistical learning theory: shattering coefficient.
Statistical Machine Learning Part 41 - Statistical learning theory: VC dimension.
Statistical Machine Learning Part 42 - Statistical learning theory: Rademacher complexity.
Statistical Machine Learning Part 43 - Statistical learning theory: consistency of regularization.
Statistical Machine Learning Part 44 - Statistical learning theory: Revisiting Occam and outlook.
Statistical Machine Learning Part 45 - ML and Society: The general debate.
Statistical Machine Learning Part 46 - ML and Society: (Un)fairness in ML.
Statistical Machine Learning Part 47 - ML and Society: Formal approaches to fairness.
Statistical Machine Learning Part 48 - ML and Society: Algorithmic approaches to fairness.
Statistical Machine Learning Part 49 - ML and Society: Explainable ML.
Statistical Machine Learning Part 50 - ML and Society: The energy footprint of ML.
Statistical Machine Learning Part 51 - Low rank matrix completion: algorithms.
Statistical Machine Learning Part 52 - Low rank matrix completion: theory.
Statistical Machine Learning Part 53 - Compressed sensing.
Statistical Machine Learning Part 54 - ML pipeline: data, preprocessing, learning.
Statistical Machine Learning Part 55 - ML pipeline: evaluation.
Taught by
Tübingen Machine Learning
