Artificial Neural Networks, Machine Learning, Deep Thinking Training Course

DAY 1 - ARTIFICIAL NEURAL NETWORKS

Introduction and ANN Structure.

• Comparison of biological and artificial neurons.
• The modeling of ANNs.
• Activation functions utilized in ANNs.
• Common categories of network architectures.

Mathematical Foundations and Learning mechanisms.

• Review of vector and matrix algebra.
• Concepts in state-space analysis.
• Principles of optimization.
• Error-correction learning.
• Memory-based learning.
• Hebbian learning.
• Competitive learning.

Single layer perceptrons.

• Architecture and training of perceptrons.
• Introduction to pattern classifiers and Bayes' classifiers.
• Using perceptrons as pattern classifiers.
• Perceptron convergence theory.
• Constraints and limitations of perceptrons.

Feedforward ANN.

• Structure of Multi-layer feedforward networks.
• The Back propagation algorithm.
• Training and convergence in back propagation.
• Functional approximation using back propagation.
• Practical considerations and design challenges in back propagation learning.

Radial Basis Function Networks.

• Pattern separability and interpolation techniques.
• Regularization Theory.
• The role of Regularization in RBF networks.
• Design and training of RBF networks.
• Approximation characteristics of RBFs.

Competitive Learning and Self organizing ANN.

• General procedures for clustering.
• Learning Vector Quantization (LVQ).
• Algorithms and architectures for competitive learning.
• Self organizing feature maps.
• Key properties of feature maps.

Fuzzy Neural Networks.

• Neuro-fuzzy systems.
• Foundations of fuzzy sets and logic.
• Designing fuzzy systems.
• Designing fuzzy ANNs.

Applications

• A discussion on several Neural Network applications, highlighting their benefits and challenges.

DAY -2 MACHINE LEARNING

• The PAC Learning Framework
  • Guarantees for finite hypothesis sets – consistent case
  • Guarantees for finite hypothesis sets – inconsistent case
  • Generalities
    • Deterministic vs. Stochastic scenarios
    • Bayes error noise
    • Estimation and approximation errors
    • Model selection
• Rademacher Complexity and VC Dimension
• Bias-Variance tradeoff
• Regularization
• 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 module is taught in conjunction with 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