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Course

MICRO-455: Machine learning I

Lectures in this course (60)

Multiclass SVM

Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.

SVM: Generalization & Confidence

Explores SVM's generalization, lack of confidence in predictions, and risk of false positives, comparing it with GMM and KNN.

Support Vector Machine Overview

Gives an overview of Support Vector Machines, comparing advantages and disadvantages of SVM with other classifiers.

Neural Networks: Perceptron

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Neural Networks: Multi-layers

Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.

Nonlinear Regression: Forecasting Trends

Explores machine learning techniques for nonlinear regression and forecasting trends in complex data sets.

Linear & Weighted Regression

Covers linear and weighted regression, limitations, and introduces locally weighted regression for model-free regression.

Ridge Regression: Regularizing Linear Models

Introduces ridge regression to regularize linear models and the kernel trick for nonlinear regression.

Support Vector Regression: Principles and Optimization

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Gaussian Mixture Regression: Modeling and Prediction

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Comparison Across Methods: GMR vs SVR

Compares GMR and SVR in machine learning, discussing their similarities, differences, and hyperparameters.

Kernel PCA: Nonlinear Dimensionality Reduction

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

Canonical Correlation Analysis: Linear and Kernel CCA

Introduces Canonical Correlation Analysis for finding joint features in separate datasets, extending to multimodal data and non-linear features.

Kernel K-means: Advanced Machine Learning

Introduces Kernel K-means, extending K-means to create non-linear separations of data points.

Kernel K-means: Analysis and Applications

Explores Kernel K-means algorithm, its analysis, applications, and limitations in clustering.

PCA: Intuition

Covers the basics of PCA, exercises on dimensionality reduction, and criteria for choosing projections.

Spectral Clustering: Theory and Applications

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Spectral Clustering and Laplacian Eigenmaps

Explores spectral clustering, Laplacian eigenmaps, and non-linear embeddings in advanced machine learning.