campUAS

1. Introduction
- IPCC: history and future
2. The big market failure?
3. Pricing carbon
4. From theory to practice -- the EU ETS
- Assessing the impact of the EU ETS
5. Subsidizing RES
Decoupling the EU ETS from demand-side effects
6. Decoupling the EU ETS from demand-side effects

This course provides an introduction into machine learning theory including mathematical foundation. It covers the following areas:

• Is statistical or machine learning possible? (Outlook Vapnik-Chervonenkis theory, VC Dimension and Limit)
  
• Statistical/Machine learning: Supervised, unsupervised, reinforcement learning, classification, regression
• Probabilistic and non-probabilistic methods
  
• Different models: 
• Linear model
• Kernel methods
• Support Vector Machines
• Bayes methods
  
• Gaussian processes
• MCMC and particle filters
• Neural networks
  
• Genetic algorithms
  
• Model selection
  
• Overfitting and regularization
• Bias-Variance tradeoff, error and noise
• Training, testing, validation o Curse of dimensionality
  
• Vapnik-Chervonenki theory, Hoeffding’s lemma, VC dimension and VC inequality
  
• Structural Risk Minimization (SRM), Occham’s Razor
  
• Overview of selected application fields
  
• Consequences of statistical learning theory
  
• Philosophic implications (Are data more important than theories? Where are the limits of statistical learning theory?
  
• Ethical implications for society, principal problems of algorithmic decisions (Information privacy, individual freedom, checks-and balances of citizens, corporations, states etc.)
  
• Lectures are accompanied by practical exercises with respect to theories and concepts, supported by the use of appropriate software