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Machine Learning

This lecture covers various topics in Machine Learning including introduction, neural networks, Bayesian learning, instance-based learning, clustering analysis, data processing and representation, support vector machines, and decision tree learning.

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Machine Learning

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  1. Machine Learning Lecture 11 Summary G53MLE | Machine Learning | Dr Guoping Qiu

  2. Topics Covered • Topic 1: Introduction • Learning and learning systems • Design of learning systems - 5 steps approach: training sample collection/preparation, data representation, choose a learning model/paradigm, learning, testing • Basic maths – vector, matrix, calculus G53MLE | Machine Learning | Dr Guoping Qiu

  3. Topics Covered • Topic 2: Neural networks • Perceptron – model operation, perceptron learning rule, decision boundary (surface), limitations, linearly separable classes • ADLINE – model operation, delta rule (gradient descent learning), batch mode, online mode • Multi-layer perceptron (MLP) - model operation, nonlinear unit (sigmoid function), principles of learning rule (back-propagation algorithm), model capability (solve linearly non-separable problems) • Generalization, over fitting, stopping criteria G53MLE | Machine Learning | Dr Guoping Qiu

  4. Topics Covered • Topic 3: Bayesian learning • Basic probability theory • Bayesian theorem • Estimation of probabilities • Maximum a posteriori (MAP) • Maximum Likelihood (ML) • Bayesian classifiers • Naïve Bayesian classifier G53MLE | Machine Learning | Dr Guoping Qiu

  5. Topics Covered • Topic 4: Instance based learning • K nearest neighbour classifier (K-NN) – feature space neighbourhood concept, classifier construction and operation, choose a suitable values of K G53MLE | Machine Learning | Dr Guoping Qiu

  6. Topics Covered • Topic 5: Clustering analysis • Basic concept of data clustering and why it is useful • How to do data clustering (K-means algorithm) – operation of the algorithm • Link between K-means algorithm and gradient descent (the concept of clustering cost function or objective function) • Limitations/weaknesses of the basic K-means algorithm – sensitive to initial cluster centres, local minima G53MLE | Machine Learning | Dr Guoping Qiu

  7. Topics Covered • Topic 6: Data processing and representation • Concepts of correlation and redundancy • Covariance matrix • Concepts of feature extraction/dimensionality reduction • Principle and application of Principal Component Analysis (PCA) G53MLE | Machine Learning | Dr Guoping Qiu

  8. Topics Covered • Topic 7: Support vector machines • Model operation (how it works) • Support vectors • Max margin classifier • Linear SVMs • Concept of Soft margin classification • Principle of Non-linear SVMs and the “Kernel Trick” G53MLE | Machine Learning | Dr Guoping Qiu

  9. Topics Covered • Topic 8: Decision tree learning • Information gain • Decision tree construction (design) – picking the root node, recursive branching G53MLE | Machine Learning | Dr Guoping Qiu

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