Lecture

Kernel Trick: Understanding Machine Learning

Related lectures (32)
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Landscape and Generalisation in Deep Learning
Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.
Kernel Regression: Weighted Average and Feature Maps
Covers kernel regression and feature maps for data separability.
Mercer Theorem and Kernels
Explores the Mercer Theorem, Kernels, and their role in machine learning applications.
Feature Maps and Kernels
Covers feature maps, Representer theorem, kernels, and RKHS in machine learning.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Kernel Methods: Machine Learning
Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
Data Representation: PCA
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Dimensionality Reduction: PCA & t-SNE
Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.