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Programma
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Programma - Types of machine learning algorithms
* Supervised Learning
* Unsupervised Learning
* Reinforcement Learning
- Regression or Classification what are them? Are they really different?
- Cost functions and their importance
* Typical regression error measures and their shortcomings.
* Classifier evaluation: Sensitivity, Specificity, Accuracy, ROC, AUC, etc.
- Datasets and Machine Learning the first and most important step
* Statistical validation
* Missing values
* Raw data: when we have to use or not to use them?
* Preprocessing
* Feature Extraction
* Feature Selection
* Feature Reduction
* Curse of dimensionality
- Model complexity
* Underfitting and overfitting
* Occam's razor principle
* Many parameters and the importance of regularization
- Singular Value Decomposition (SVD)
* Linear modeling is not infrequently enough
* Lowering machine learning algorithm coomplexity
* SVD/PCA as feature reduction but sometimes fails
- Neural Networks
* The biological neuron
* The artifical neuron
* Network topology
* The Multilayer Perceptron
* The universal approximation theorem
* Fixed, Self Adaptive, and Stochastic Gradient descend as a general technique
for parameter estimation
* Backpropagation
* Radial Basis Functions
* Deep Learning
- Cluster Analysis and Vector Quantization
* K-means/LBG algorithm
= Serial improvements
Escaping from local minima: the Enhanced LBG Algorithm (ELBG)
From target error to clusters: Fully Automatic Clustering system (FACS)
= Big data and parallel clustering
Parallel algorithms for unsupervised learning (PAUL)
Very large data sets vector quantization (LBGS)
* Other clustering approaches: Hierarchical Clustering and Fuzzy Clustering
- Global optimization inspired by biological evolution
* From Monte Carlo methods to Evolutionary Computation
= The Population: A set of candidate solutions as individuals
= Selection among individuals: Roulette, Ordering, Tournament.
= Generation of new solutions: offsprings
Recombination/crossover
Mutation
Hill-climbing
= Multi-objective optimization: The Fitness Function
* Evolutionary techniques, some examples
= Genetic Algorithms and Holland's schema theorem
= Genetic Programming
= Parallel/Distributed Genetic Programming for Mathematical Modelling: The Brain Project
* Case study: Find the minimum of the function $y=\sum_{i=1}^1000$ (x_i-1000/i)^2 with $x_i in [0,2]$
- Fuzzy logic from classical boolean logic to many-valued logic.
* Fuzzy sets and membership functions.
* Operations on Fuzzy sets.
* Fuzzy relations, rules, propositions, implications and inferences.
* Defuzzification techniques.
* Fuzzy logic controller design.
- Hybridization is often the way to get better results
- Case studies in physics
* Data Analysis of Gravitation Wave time series
* Track recognition in Nuclear Physics Collisions
* Structure of the proton using contemporary methods of artificial intelligence
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