Machine Learning

Semester:	Sommersemester 2015
Veranstalter:	Prof. Leibe
Bemerkungen:	Course website with dates, slides, exercises and literature L2P learning room The screencasts produced by Prof. Leibe will usually be uploaded the same or the next day. Cutting the recorded videos, however, takes time, so they might take a week or two to be uploaded.

Videos

- Introduction
- Do, 09.04.2015, 14:15 Uhr
- Introduction, Probability Theory, Bayes Decision Theory, Minimizing Expected Loss
- Prob. Density Estimation I
- Do, 16.04.2015, 14:15 Uhr
- Parametric Methods, Gaussian Distribution, Maximum Likelihood
- Parametric Methods
- Prob. Density Estimation II
- Di, 21.04.2015, 14:15 Uhr
- Bayesian Learning, Nonparametric Methods, Histograms, Kernel Density Estimation
- Non-Parametric Methods
- Prob. Density Estimation III
- Do, 23.04.2015, 14:15 Uhr
- Mixture of Gaussians, k-Means Clustering, EM-Clustering, EM Algorithm
- Linear Discriminant Functions I
- Di, 28.04.2015, 14:15 Uhr
- Linear Discriminant Functions, Least-squares Classification, Generalized Linear Models
- Recap: Mixture Models
- Linear Discriminant Functions
- Extension to Multiple Classes
- further questions on the linear discriminant model
- Least-Squares Classification
- Problems with Least Squares
- Generalized Linear Models
- Relationship to Neural Networks
- Linear Discriminant Functions II
- Di, 05.05.2015, 14:15 Uhr
- Fisher Linear Discriminants, Logistic Regression, Iteratively Reweighted Least Squares
- Statistical Learning Theory
- Do, 07.05.2015, 14:15 Uhr
- Statistical Learning Theory, VC Dimension, Structural Risk Minimization
- Linear SVMs
- Di, 12.05.2015, 14:15 Uhr
- Linear SVMs, Soft-margin classifiers, nonlinear basis functions
- Non-Linear SVMs
- Di, 19.05.2015, 14:15 Uhr
- Kernel trick, Mercer's condition, Nonlinear SVMs, Support Vector Data Description
- Ensemble Methods and Boosting
- Di, 02.06.2015, 14:15 Uhr
- Model Combination, Bagging, Boosting, AdaBoost, Sequential Additive Minimization
- AdaBoost
- Di, 09.06.2015, 14:15 Uhr
- AdaBoost, Exponential error, Applications, Decision Trees, CART framework, ID3, C4.5
- Randomized Trees
- Do, 11.06.2015, 14:15 Uhr
- Randomized Decision Trees, Random Forests, Extremely Randomized Trees, Ferns
- Graphical Models I
- Di, 16.06.2015, 14:15 Uhr
- Intro to Graphical Models, Bayesian Networks, Conditional Independence, Bayes Ball algorithm
- Graphical Models II
- Di, 23.06.2015, 14:15 Uhr
- Markov Random Fields, Converting directed to undirected models
- Graphical Models III
- Do, 25.06.2015, 14:15 Uhr
- Exact Inference, Message Passing, Belief Propagation, Factor Graphs, Sum-product algorithm
- Applying MRFs
- Di, 30.06.2015, 14:15 Uhr
- Junction Tree Algorithm, Loopy BP, Applications of MRFs
- Solving MRFs with Graph Cuts
- Gehalten von Ishrat Badami
- Di, 07.07.2015, 14:15 Uhr
- Solving MRFs with Graph Cuts, s-t Mincut, Alpha Expansion
- Repetition
- Di, 14.07.2015, 14:15 Uhr