- 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 steps.
- 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
- 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 $x_i in [0,2]$

- From Monte Carlo methods to Evolutionary Computation
- 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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- K-means/LBG algorithm

- Model complexity.