Advanced Machine Learning
Semester: |
WS 2012 |
Type: |
Lecture |
Lecturer: |
|
Credits: |
None |
Note: This page is for a course from a previous semester.
Find a list of current courses on the Teaching page.
Find a list of current courses on the Teaching page.
This lecture will extend the scope of the "Machine Learning" lecture with additional and, in parts, more advanced concepts. In particular, the lecture will cover the following areas:
- Regression techniques (linear regression, ridge regression, lasso, support vector regression)
- Gaussian Processes
- Learning with latent variables
- Dirichlet Processes
- Structured output learning
| Date | Title | Content | Material |
|---|---|---|---|
| Introduction | Introduction, Polynomial Fitting, Least-Squares Regression, Overfitting, Regularization, Ridge Regression | ||
| Linear Regression I | Probabilistic View of Regression, Maximum Likelihood, MAP, Bayesian Curve Fitting | ||
| Linear Regression II | Basis Functions, Sequential Learning, Multiple Outputs, Regularization, Lasso, Bias-Variance Decomposition | ||
| Gaussian Processes I | Kernels, Kernel Ridge Regression, Gaussian Processes, Predictions with noisy observations | ||
| Gaussian Processes II | Influence of hyperparameters, Bayesian Model Selection, GP Classification | ||
| Probability Distributions | Bayesian Estimation Revisited, Conjugate Priors, Bernoulli, Binomial, Beta, Multinomial, Dirichlet, Gaussian, Gamma, Student's t | ||
| Approximate Inference I | Sampling Approaches, Monte Carlo Methods, Transformation Methods, Ancestral Sampling, Rejection Sampling, Importance Sampling | ||
| Approximate Inference II | Markov Chain Monte Carlo, Metropolis-Hastings Algorithm, Gibbs Sampling | ||
| Mixture Models I | Mixtures of Gaussians, ML estimation, EM, latent variables, General EM | ||
| Mixture Models II | General EM, Mixtures of Bernoulli distributions, Monte Carlo EM, Dirichlet priors, Infinite Mixture Models, Gibbs Sampling | ||
| Dirichlet Processes | Definition, Properties, Polya Urn scheme, Chinese Restaurant Process | ||
| Dirichlet Processes II | De Finetti's Theorem, Exchangeability, Stick-Breaking Construction, DP Mixture Models | ||
| Hierarchical DPs | Gibbs Sampling for DPMMs, Hierarchical DPs, Chinese Restaurant Franchise | ||
| Hierarchical DPs II | Gibbs Sampling for HDPs, Applications | ||
| Latent Factor Models | Latent Factor Models, PCA, Probabilistic PCA, ML for PCA, Factor Analysis, ICA, Sparse Latent Factor Models | ||
| Beta Processes I | Finite Latent Factor Models, Infinite Latent Factor Models, Indian Buffet Process | ||
| Beta Processes II | Beta Processes, Stick-Breaking Construction, Gibbs Sampling for BPs, Applications, Nonparametric HMMs | ||
| Support Vector Machines | SVMs revisited, Loss functions | ||
| SV Regression | Kernel PCA, Kernel k-Means, Support Vector Data Description, Support Vector Regression | ||
| Structured Output Learning | General structured prediction, Loss functions, Structured Output SVM, Cutting plane training | ||
| Structured Output Learning II | One-Slack formulation, Multi-class SVM, Joint kernel function, Kernelized S-SVM |
