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Duration 21 hours
Course Outline
DAY 1 - ARTIFICIAL NEURAL NETWORKS
Introduction and ANN Architecture.
- Biological neurons versus artificial neurons.
- Theoretical modeling of ANNs.
- Activation functions applied in ANNs.
- Standard classes of network architectures.
Mathematical Foundations and Learning Mechanisms.
- Review of vector and matrix algebra.
- State-space formulations.
- Optimization principles.
- Error-correction methodologies.
- Memory-based approaches.
- Hebbian learning processes.
- Competitive learning frameworks.
Single Layer Perceptrons.
- Perceptron structure and learning dynamics.
- Pattern classification fundamentals and Bayes' classifiers.
- Application of perceptrons as pattern classifiers.
- Perceptron convergence properties.
- Operational limitations of perceptrons.
Feedforward ANN.
- Architectural design of multi-layer feedforward networks.
- Backpropagation algorithms.
- Backpropagation: training procedures and convergence criteria.
- Functional approximation techniques using backpropagation.
- Practical considerations and design issues in backpropagation.
Radial Basis Function Networks.
- Pattern separability and interpolation techniques.
- Theory of regularization.
- Application of regularization to RBF networks.
- Design and training of RBF networks.
- Approximation capabilities of RBF models.
Competitive Learning and Self-Organizing ANN.
- General clustering procedures.
- Learning Vector Quantization (LVQ).
- Competitive learning algorithms and network architectures.
- Self-Organizing Feature Maps.
- Characteristics 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
- Examination of neural network implementations, including their benefits and operational challenges for government contexts.
DAY 2 - MACHINE LEARNING
- The PAC Learning Framework
- Guarantees for finite hypothesis sets – consistent case
- Guarantees for finite hypothesis sets – inconsistent case
- General considerations
- Deterministic versus stochastic scenarios
- Bayes error noise
- Estimation and approximation errors
- Model selection strategies
- Rademacher Complexity and VC-Dimension
- Bias-Variance tradeoff
- Regularization techniques
- Overfitting mitigation
- Validation methodologies
- 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 structured in relation to 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
- Demonstrations and practical applications
Requirements
A strong understanding of mathematics.
A solid grasp of basic statistics.
Basic programming skills are not mandatory but are recommended.
Testimonials (2)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
