An introduction to machine learning course provides foundational knowledge in the field of machine learning, covering essential concepts, algorithms, and practical applications. These courses typically explore supervised and unsupervised learning techniques, including linear and logistic regression, decision trees, and clustering.
Curriculum
- 5 Sections
- 14 Lessons
- 10 Weeks
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- Module 12 ItemsBasics 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
- Module 23 ItemsRegression: 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.
- Module 32 ItemsLinear discriminant based algorithm: perceptron, gradient descent method, perceptron algorithm, support vector machines, separable classes, non-separable classes, multiclass case.
- Module 44 ItemsUnsupervised learning: Clustering, examples, criterion functions for clustering, proximity measures, algorithms for clustering. Ensemble methods: boosting, bagging. Basics of decision trees, random forest, examples.
- Module 53 ItemsDimensionality 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.
