Course Outline
DAY 1 - ARTIFICIAL NEURAL NETWORKS
Introduction and ANN Structure.
- Comparison between biological neurons and artificial neurons.
- The structural model of an ANN.
- Activation functions utilized in ANNs.
- Common categories of network architectures.
Mathematical Foundations and Learning mechanisms.
- Review of vector and matrix algebra.
- State-space concepts.
- Principles of optimization.
- Learning via error correction.
- Memory-based learning approaches.
- Hebbian learning.
- Competitive learning strategies.
Single layer perceptrons.
- Structure and learning processes of perceptrons.
- Introduction to pattern classification and Bayes' classifiers.
- Utilizing perceptrons as pattern classifiers.
- Convergence of the perceptron.
- Constraints inherent to perceptrons.
Feedforward ANN.
- Structures 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 learning.
Radial Basis Function Networks.
- Pattern separability and interpolation techniques.
- Theory of regularization.
- Regularization applied to RBF networks.
- Design and training of RBF networks.
- Approximation properties of RBFs.
Competitive Learning and Self organizing ANN.
- General clustering procedures.
- Learning Vector Quantization (LVQ).
- Algorithms and architectures for competitive learning.
- Self-organizing feature maps.
- Key properties of feature maps.
Fuzzy Neural Networks.
- Neuro-fuzzy systems.
- Foundations of fuzzy sets and logic.
- Design of fuzzy stems.
- Design of fuzzy ANNs.
Applications
- Discussion of select Neural Network applications, highlighting their benefits and potential 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 vs. 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 module 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
A solid grasp of mathematical principles.
A strong understanding of fundamental statistics.
While basic programming skills are not mandatory, they are strongly 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.
