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 structural model of an ANN.
• Activation functions commonly utilized in ANNs.
• Standard classes of network architectures.

Mathematical Foundations and Learning mechanisms.

• Review of vector and matrix algebra.
• State-space concepts.
• Key concepts in optimization.
• Error-correction learning methods.
• Memory-based learning approaches.
• Hebbian learning principles.
• Competitive learning strategies.

Single layer perceptrons.

• Perceptron structure and learning processes.
• Introduction to pattern classifiers and Bayes' classifiers.
• Utilizing perceptrons as pattern classifiers.
• Perceptron convergence properties.
• Inherent limitations of perceptrons.

Feedforward ANN.

• Architecture 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.

Radial Basis Function Networks.

• Pattern separability and interpolation techniques.
• Theory of Regularization.
• Application of Regularization to RBF networks.
• Designing and training RBF networks.
• Approximation capabilities of RBF models.

Competitive Learning and Self organizing ANN.

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

Fuzzy Neural Networks.

• Neuro-fuzzy hybrid systems.
• Fundamentals of fuzzy sets and logic.
• Designing fuzzy systems.
• Construction of fuzzy ANNs.

Applications

• Discussion of various Neural Network applications, highlighting their benefits 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 considerations
    • Deterministic vs. Stochastic scenarios
    • Bayes error noise
    • Estimation and approximation errors
    • Model selection strategies
• Rademacher Complexity and VC Dimension
• The Bias-Variance tradeoff
• Regularization techniques
• Over-fitting issues
• 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 will be contextualized with topics covered in Days 1 and 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 Real-world Applications