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Course Outline
DAY 1 - ARTIFICIAL NEURAL NETWORKS
Introduction and ANN Structure.
- Biological neurons and artificial neurons.
- Model of an ANN.
- Activation functions used in ANNs.
- Typical classes of network architectures .
Mathematical Foundations and Learning mechanisms.
- Re-visiting vector and matrix algebra.
- State-space concepts.
- Concepts of optimization.
- Error-correction learning.
- Memory-based learning.
- Hebbian learning.
- Competitive learning.
Single layer perceptrons.
- Structure and learning of perceptrons.
- Pattern classifier - introduction and Bayes' classifiers.
- Perceptron as a pattern classifier.
- Perceptron convergence.
- Limitations of a perceptrons.
Feedforward ANN.
- Structures of Multi-layer feedforward networks.
- Back propagation algorithm.
- Back propagation - training and convergence.
- Functional approximation with back propagation.
- Practical and design issues of back propagation learning.
Radial Basis Function Networks.
- Pattern separability and interpolation.
- Regularization Theory.
- Regularization and RBF networks.
- RBF network design and training.
- Approximation properties of RBF.
Competitive Learning and Self organizing ANN.
- General clustering procedures.
- Learning Vector Quantization (LVQ).
- Competitive learning algorithms and architectures.
- Self organizing feature maps.
- Properties of feature maps.
Fuzzy Neural Networks.
- Neuro-fuzzy systems.
- Background of fuzzy sets and logic.
- Design of fuzzy stems.
- Design of fuzzy ANNs.
Applications
- A few examples of Neural Network applications, their advantages and problems will be discussed.
DAY -2 MACHINE LEARNING
- The PAC Learning Framework
- Guarantees for finite hypothesis set – consistent case
- Guarantees for finite hypothesis set – inconsistent case
- Generalities
- Deterministic cv. Stochastic scenarios
- Bayes error noise
- Estimation and approximation errors
- Model selection
- Radmeacher Complexity and VC – Dimension
- 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
This will be 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
Requirements
Good understanding of mathematics.
Good understanding of basic statistics.
Basic programming skills are not required but recommended.
21 Hours
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.
