MACHINE LEARNING FOR PHYSICS

Course Content.

• 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
          • 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

      Any student, who finds inaccuracies, can kindly send an email to the teacher, who will make the necessary corrections. THANK YOU!!!