Artificial Neural Networks, Machine Learning, Deep Thinking Training Course

DAY 1 - ARTIFICIAL NEURAL NETWORKS

Introduction and ANN Structure.

• Overview of biological neurons and artificial neurons.
• Structure and components of an Artificial Neural Network (ANN).
• Activation functions utilized in ANNs.
• Common types of network architectures.

Mathematical Foundations and Learning Mechanisms.

• Review of vector and matrix algebra concepts.
• Introduction to state-space concepts.
• Fundamentals of optimization techniques.
• Error-correction learning methods.
• Memory-based learning approaches.
• Hebbian learning principles.
• Competitive learning strategies.

Single Layer Perceptrons.

• Structure and learning processes of perceptrons.
• Introduction to pattern classification, including Bayes' classifiers.
• Perceptron as a tool for pattern classification.
• Convergence properties of the perceptron algorithm.
• Limits and constraints of single-layer perceptrons.

Feedforward ANN.

• Structures of multi-layer feedforward networks.
• The back propagation algorithm for training neural networks.
• Convergence and practical considerations in back propagation.
• Functional approximation using back propagation techniques.
• Design and implementation issues in back propagation learning.

Radial Basis Function Networks.

• Pattern separability and interpolation methods.
• Theory of regularization in machine learning.
• Application of regularization in Radial Basis Function (RBF) networks.
• Design and training procedures for RBF networks.
• Approximation capabilities of RBF networks.

Competitive Learning and Self-Organizing ANN.

• General clustering methodologies.
• Learning Vector Quantization (LVQ) techniques.
• Algorithms and architectures for competitive learning.
• Self-organizing feature maps and their properties.

Fuzzy Neural Networks.

• Introduction to neuro-fuzzy systems.
• Background on fuzzy sets and logic.
• Design principles of fuzzy systems.
• Integration of fuzzy logic with artificial neural networks.

Applications

• Case studies of Neural Network applications, highlighting their benefits and challenges.

DAY 2 - MACHINE LEARNING

• The PAC Learning Framework
  • Guarantees for finite hypothesis sets in consistent scenarios.
  • Guarantees for finite hypothesis sets in inconsistent scenarios.
  • General principles
    • Deterministic versus stochastic learning environments.
    • Bayes error and noise considerations.
    • Estimation and approximation errors in model training.
    • Strategies for model selection.
• Rademacher Complexity and VC-Dimension.
• Bias-Variance tradeoff in machine learning models.
• Regularization techniques to prevent overfitting.
• Overfitting prevention methods.
• Validation strategies for model evaluation.
• Support Vector Machines (SVM) for classification and regression.
• Kriging (Gaussian Process regression) for spatial data analysis.
• Principal Component Analysis (PCA) and Kernel PCA for dimensionality reduction.
• Self-Organizing Maps (SOM) for unsupervised learning.
• Kernel-induced vector space
  • Mercer Kernels and their role in defining similarity metrics.
• Reinforcement Learning for decision-making systems.

DAY 3 - DEEP LEARNING

This will be taught in relation to the topics covered on Day 1 and Day 2.

• Logistic and Softmax Regression for classification tasks.
• Sparse Autoencoders for efficient data representation.
• Vectorization, Principal Component Analysis (PCA), and Whitening techniques.
• Self-Taught Learning for unsupervised feature learning.
• Deep Networks for complex pattern recognition.
• Linear Decoders for decoding neural signals.
• Convolution and Pooling operations in deep networks.
• Sparse Coding for efficient data encoding.
• Independent Component Analysis (ICA) for signal processing.
• Canonical Correlation Analysis (CCA) for multivariate analysis.
• Demos and practical applications of deep learning techniques for government use cases.