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 conceptual model of an ANN.
• Activation functions employed in ANNs.
• Common categories of network architectures.

Mathematical Foundations and Learning mechanisms.

• Review of vector and matrix algebra.
• State-space representations.
• Core concepts of optimization.
• Error-correcting learning paradigms.
• Memory-based learning approaches.
• Hebbian learning principles.
• Competitive learning strategies.

Single layer perceptrons.

• Architectural structure and learning processes of perceptrons.
• Introduction to pattern classification and Bayes' classifiers.
• Utilizing perceptrons as pattern classifiers.
• The convergence of the perceptron.
• Inherent limitations of perceptrons.

Feedforward ANN.

• Structure of Multi-layer feedforward networks.
• The Back propagation algorithm.
• Back propagation: training dynamics and convergence.
• Functional approximation via back propagation.
• Practical considerations and design issues in back propagation.

Radial Basis Function Networks.

• Pattern separability and interpolation techniques.
• Theory of Regularization.
• Application of Regularization in RBF networks.
• Design and training strategies for RBF networks.
• Approximation characteristics of RBFs.

Competitive Learning and Self organizing ANN.

• General clustering methodologies.
• Learning Vector Quantization (LVQ).
• Competitive learning algorithms and related architectures.
• Self-organizing feature maps.
• Key properties of feature maps.

Fuzzy Neural Networks.

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

Applications

• Discussion of select Neural Network applications, highlighting their advantages and potential challenges.

DAY 2 - MACHINE LEARNING

• The PAC Learning Framework
  • Guarantees for finite hypothesis sets in consistent cases
  • Guarantees for finite hypothesis sets in inconsistent cases
  • General principles
    • Deterministic vs. Stochastic scenarios
    • Bayes error noise
    • Estimation and approximation errors
    • Model selection
• Rademacher Complexity and VC-Dimension
• The Bias-Variance tradeoff
• Regularization techniques
• Overfitting prevention
• Validation methods
• Support Vector Machines
• Kriging (Gaussian Process regression)
• PCA and Kernel PCA
• Self-Organizing Maps (SOM)
• Kernel-induced vector spaces
  • Mercer Kernels and Kernel-induced similarity metrics
• Reinforcement Learning

DAY 3 - DEEP LEARNING

Content is taught in context with topics from 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