ECT463 Machine Learning → Classification

ECT463 Machine Learning

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Module 1
Basics of machine learning, supervised and unsupervised learning, examples, features, feature vector, training set, target vector, test set, feature extraction, over-fitting, curse of dimensionality. Review of probability theory, Gaussian distribution, decision theory
2
- 1.1
  Basics of Machine Learning
- 1.2
  Notes
Module 2
Regression: linear regression, error functions in regression, multivariate regression, regression applications, bias and variance. Classification : Bayes’ decision theory, discriminant functions and decision surfaces, Bayesian classification for normal distributions, classification applications.
3
Module 3
Linear discriminant based algorithm: perceptron, gradient descent method, perceptron algorithm, support vector machines, separable classes, non-separable classes, multiclass case.
2
- 3.1
  LDA, Gradient Descent, Percptron, SVM
- 3.2
  Notes
Module 4
Unsupervised learning: Clustering, examples, criterion functions for clustering, proximity measures, algorithms for clustering. Ensemble methods: boosting, bagging. Basics of decision trees, random forest, examples.
4
Module 5
Dimensionality reduction: principal component analysis, Fischer's discriminant analysis. Evaluation and model Selection: ROC curves, evaluation measures, validation set, biasvariance trade-off. Confusion matrix, recall, precision, accuracy.
3

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Regression