曙海科技
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Artificial Neural Networks, Machine Learning, Deep Thinking培训
 
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   授课地点及时间
上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【广州分部】:广粮大厦 【西安分部】:协同大厦 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦
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课程大纲
 
  • 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
 
 
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