Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA
Graph Chatbot
Related lectures (30)
Previous
Page 3 of 3
Next
Machine Learning Fundamentals
Covers key concepts and examples of machine learning algorithms and techniques.
Linear Dimensionality Reduction: PCA and LDA
Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.
Machine Learning: Supervised and Unsupervised Learning Techniques
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Unsupervised Learning: Movie Recommendation
Covers unsupervised learning for movie recommendation using singular value decomposition.
Supervised Learning Overview
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Dimensionality Reduction: PCA and Autoencoders
Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.
Textual Data Analysis: Classification & Dimensionality Reduction
Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.
Singular Value Decomposition
Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Unsupervised Learning: Clustering Methods
Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.