EE-311: Fundamentals of machine learning

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Lectures in this course (48)

SVM and Multiclass Classification

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Redescription SVM and Hilbert Spaces

Explores redescription SVM and Hilbert spaces for solving nonlinear problems using inner product spaces and separable Hilbert spaces.

Support Vector Machines: Kernel Tricks

Explores kernel tricks in support vector machines for efficient computation in high-dimensional spaces without explicit transformation.

Dimension Reduction: Curse of Dimensionality

Explores dimension reduction and the curse of dimensionality, highlighting the exponential relation between examples and dimension.

Variable Selection Methods: Filtering and Correlation

Explores variable selection through filtering and correlation methods in machine learning, emphasizing relevance quantification and relationship measurement with the label.

Training and Testing Games

Emphasizes the importance of separating training and testing data for machine learning models.

Variable Selection Methods: Subset vs. Container Approaches

Explores variable selection methods in machine learning, including subset and container approaches, using exhaustive and greedy procedures.

Principal Component Analysis: Geometric Interpretation and Dimension Reduction

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Introduction to Clustering: Methods and Applications

Covers the fundamentals of clustering in unsupervised learning and its practical applications.

Characterisation of Clusters: Homogeneity, Separability

Explores centroid, medoid, homogeneity, separability in clustering, quality evaluation, stability, expert knowledge, and clustering algorithms.

Hierarchical Clustering: Dendrograms and Linkage Functions

Explores hierarchical clustering, dendrograms, and various linkage functions for cluster agglomeration based on distance measures.

K-means Clustering: Lloyd's Algorithm and RGB Space

Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.

Clustering Methods: K-means and Density Clustering

Explores k-means, kernel trick, and density clustering methods for non-convex clusters.

Multi-layered Perceptron: History and Training Algorithm

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Decision Trees and Boosting

Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.

Densities and Bayesian Inference: Decision Rules and Bayes Law

Covers classification concepts, medical screening tests, and decision rules.

Decision Rules: Maximum a Posteriori Decision

Explores decision rules based on likelihood ratios and the maximum a posteriori decision.

Sexing Guppy Fish: Bayesian Inference and Decision Rules

Covers the sexing of guppy fish using Bayesian inference and decision rules.

Bayesian Decision Theory: Utility, Risk, and Classification

Covers Bayesian decision theory, cost functions, classification, and decision rules.

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