Self-supervised learning

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Related lectures (31)

Page 2 of 4

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Machine Learning Fundamentals

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.