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• DAY 1 - ARTIFICIAL NEURAL NETWORKS
  Introduction and ANN Structure.
  Biological neurons and artificial neurons.
  Model of an ANN.
  Activation functions used in ANNs.
  Typical classes of network architectures .
  Mathematical Foundations and Learning mechanisms.
  Re-visiting vector and matrix algebra.
  State-space concepts.
  Concepts of optimization.
  Error-correction learning.
  Memory-based learning.
  Hebbian learning.
  Competitive learning.
  Single layer perceptrons.
  Structure and learning of perceptrons.
  Pattern classifier - introduction and Bayes' classifiers.
  Perceptron as a pattern classifier.
  Perceptron convergence.
  Limitations of a perceptrons.
  Feedforward ANN.
  Structures of Multi-layer feedforward networks.
  Back propagation algorithm.
  Back propagation - training and convergence.
  Functional approximation with back propagation.
  Practical and design issues of back propagation learning.
  Radial Basis Function Networks.
  Pattern separability and interpolation.
  Regularization Theory.
  Regularization and RBF networks.
  RBF network design and training.
  Approximation properties of RBF.
  Competitive Learning and Self organizing ANN.
  General clustering procedures.
  Learning Vector Quantization (LVQ).
  Competitive learning algorithms and architectures.
  Self organizing feature maps.
  Properties of feature maps.
  Fuzzy Neural Networks.
  Neuro-fuzzy systems.
  Background of fuzzy sets and logic.
  Design of fuzzy stems.
  Design of fuzzy ANNs.
  Applications
  A few examples of Neural Network applications, their advantages and problems will be discussed.
  DAY -2 MACHINE LEARNING
  The PAC Learning Framework
  Guarantees for finite hypothesis set – consistent case
  Guarantees for finite hypothesis set – inconsistent case
  Generalities
  Deterministic cv. Stochastic scenarios
  Bayes error noise
  Estimation and approximation errors
  Model selection
  Radmeacher Complexity and VC – Dimension
  Bias - Variance tradeoff
  Regularisation
  Over-fitting
  Validation
  Support Vector Machines
  Kriging (Gaussian Process regression)
  PCA and Kernel PCA
  Self Organisation Maps (SOM)
  Kernel induced vector space
  Mercer Kernels and Kernel - induced similarity metrics
  Reinforcement Learning
  DAY 3 - DEEP LEARNING
  This will be taught in relation to the topics covered on Day 1 and Day 2
  Logistic and Softmax Regression
  Sparse Autoencoders
  Vectorization, PCA and Whitening
  Self-Taught Learning
  Deep Networks
  Linear Decoders
  Convolution and Pooling
  Sparse Coding
  Independent Component Analysis
  Canonical Correlation Analysis
  Demos and Applications