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 architectural model of an ANN.
• Activation functions utilized in ANNs.
• Common network architecture classes.

Mathematical Foundations and Learning mechanisms.

• Review of vector and matrix algebra.
• Understanding state-space concepts.
• Core concepts in optimization.
• Error-correction learning principles.
• Memory-based learning approaches.
• Hebbian learning dynamics.
• Competitive learning strategies.

Single layer perceptrons.

• Perceptron structure and learning processes.
• Introduction to pattern classification and Bayes' classifiers.
• Utilizing perceptrons as pattern classifiers.
• The perceptron convergence theorem.
• Inherent limitations of single-layer perceptrons.

Feedforward ANN.

• Architecture of multi-layer feedforward networks.
• The backpropagation algorithm.
• Training and convergence via backpropagation.
• Functional approximation using backpropagation.
• Practical considerations and design challenges in backpropagation learning.

Radial Basis Function Networks.

• Pattern separability and interpolation techniques.
• Theory of regularization.
• Regularization applied to RBF networks.
• Designing and training RBF networks.
• Approximation capabilities of RBFs.

Competitive Learning and Self organizing ANN.

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

Fuzzy Neural Networks.

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

Applications

• Discussion of select 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
• Raodeacher 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 session will connect concepts with those 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