Artificial Neural Networks, Machine Learning, Deep Thinking
Corso
A Milano
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Descrizione
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Tipologia
Corso
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Luogo
Milano
Artificial Neural Network è un modello di dati computazionale utilizzato nello sviluppo di sistemi di Intelligenza Artificiale (AI) in grado di svolgere compiti "intelligenti". Le reti neurali sono comunemente utilizzate nelle applicazioni di Machine Learning (ML), che sono esse stesse un'implementazione dell'IA. Apprendimento profondo è un sottoinsieme di ML.
Machine Translated
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Profilo del corso
Good understanding of mathematics.
Good understanding of basic statistics.
Basic programming skills are not required but recommended.
Opinioni
Materie
- E-learning
Programma
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 .
- Re-visiting vector and matrix algebra.
- State-space concepts.
- Concepts of optimization.
- Error-correction learning.
- Memory-based learning.
- Hebbian learning.
- Competitive learning.
- Structure and learning of perceptrons.
- Pattern classifier - introduction and Bayes' classifiers.
- Perceptron as a pattern classifier.
- Perceptron convergence.
- Limitations of a perceptrons.
- 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.
- Pattern separability and interpolation.
- Regularization Theory.
- Regularization and RBF networks.
- RBF network design and training.
- Approximation properties of RBF.
- General clustering procedures.
- Learning Vector Quantization (LVQ).
- Competitive learning algorithms and architectures.
- Self organizing feature maps.
- Properties of feature maps.
- Neuro-fuzzy systems.
- Background of fuzzy sets and logic.
- Design of fuzzy stems.
- Design of fuzzy ANNs.
- A few examples of Neural Network applications, their advantages and problems will be discussed.
- 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
- 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
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Artificial Neural Networks, Machine Learning, Deep Thinking