Artificial Neural Networks, Machine Learning, Deep Thinking Training Course

DAY 1 - ARTIFICIAL NEURAL NETWORKS

Introduction and ANN Structure.

• The relationship between biological neurons and artificial neurons.
• The structural model of an ANN.
• Activation functions employed in ANNs.
• Common classifications of network architectures.

Mathematical Foundations and Learning Mechanisms.

• A review of vector and matrix algebra.
• Concepts related to state-space.
• Principles of optimization.
• Error-correction learning.
• Memory-based learning.
• Hebbian learning.
• Competitive learning.

Single Layer Perceptrons.

• The structure and learning process of perceptrons.
• Introduction to pattern classifiers and Bayes' classifiers.
• The perceptron's role as a pattern classifier.
• Perceptron convergence.
• The limitations inherent in perceptrons.

Feedforward ANN.

• The structure of multi-layer feedforward networks.
• The backpropagation algorithm.
• Backpropagation regarding training and convergence.
• Functional approximation using backpropagation.
• Practical and design considerations for backpropagation learning.

Radial Basis Function Networks.

• Pattern separability and interpolation.
• Theory of Regularization.
• Regularization in the context of RBF networks.
• Design and training of RBF networks.
• The approximation capabilities of RBF.

Competitive Learning and Self-organizing ANN.

• General clustering methods.
• Learning Vector Quantization (LVQ).
• Competitive learning algorithms and their architectures.
• Self-organizing feature maps.
• Characteristics of feature maps.

Fuzzy Neural Networks.

• Neuro-fuzzy systems.
• The background of fuzzy sets and logic.
• Designing fuzzy stems.
• Designing fuzzy ANNs.

Applications

• A discussion of select Neural Network applications, highlighting their advantages and associated 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 versus Stochastic scenarios
    • Bayes error noise
    • Estimation and approximation errors
    • Model selection
• Rademacher Complexity and VC – Dimension
• The 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

Content is 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