Synopsis
Introduction to Machine Learning (2 lectures): Overview of Machine Learning. General issues: Concept learning, Classification and Classifiers, Supervised and unsupervised learning. Instance-based learning (K-Nearest Neighbour). Clustering. Learning using decision trees.
Neural networks (4 lectures): Perceptron networks, Multi-Layer Perceptron networks, Self-Organizing Maps, Hopfield networks, Radial Basis Function networks, Support Vector Machines.
Fuzzy Sets and Systems (3 lectures): Fuzzy sets. Learning through fuzzy logic, Fuzzy inference, Fuzzy modelling and optimization. Learning based on rough sets.
Evolutionary algorithms (3 lectures): Genetic algorithms, Evolutionary strategies, Genetic programming, Ant colony optimization algorithms, Particle swarm optimization.
Hybrid intelligent methods (1 lecture): Neuro-fuzzy systems, Neuro-symbolic systems, Genetic algorithms-based hybrid systems, Other hybrid learning approaches.
Inductive Logic Programming (1 lecture): Inductive learning. Applications of ILP to real-world problems.
Ensemble methods (1 lecture): Bagging, Boosting, methods of combining classifiers, classifier diversity, topologies of multi-classifier systems.
Evaluating models and algorithms. Computational Learning Theory (1 lecture): Model selection, Feature selection, ROC analysis. PAC Learnability, The Vapnik-Chervonenkis dimension.
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