Multi-Class Classification: Approaches and Boundaries

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Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Logistic Regression: Part 1

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Feature Extraction & Clustering Methods

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Classification with GMM

Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.

Gradient Descent: MNIST Dataset and Logistic Loss

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.