Lecture
Advanced Machine Learning: Feature Selection
In course
DEMO: excepteur qui occaecat veniam
In ullamco excepteur occaecat quis amet enim incididunt commodo fugiat esse non incididunt. Reprehenderit deserunt et aliqua tempor dolore est consectetur est proident. Irure dolor tempor ipsum nulla velit dolor laboris enim esse non sunt anim amet. Lorem aliqua deserunt amet dolor excepteur et incididunt eu ad non do aliquip Lorem. Commodo id aute excepteur culpa sit ea sint culpa. Magna nulla sit cillum veniam. Ex et consectetur commodo minim ex.
Description
This lecture covers the fundamental components of machine learning algorithms, including input description, internal representation, and decision mechanisms. It delves into feature selection techniques such as FAST and BRIEF descriptors, emphasizing the importance of selecting relevant features in large datasets. The instructor also discusses the limitations of deep learning, highlighting issues like algorithmic bias, lack of diversity, and susceptibility to deception, urging the audience to explore a variety of machine learning techniques beyond deep learning for a more comprehensive understanding of the field.
Instructor
adipisicing ullamco nisi minim
Ad elit laborum voluptate commodo irure cupidatat enim sint consequat commodo sit aliquip. Eu veniam Lorem tempor exercitation aute commodo deserunt deserunt nisi aliquip culpa culpa. Voluptate ad nisi irure eu aute enim est irure amet. Qui nisi pariatur sint minim ipsum.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (37)
Learning Sparse Features: Overfitting in Neural Networks
Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.
Landscape and Generalisation in Deep Learning
Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.
Feature Engineering: Polynomial Regression
Covers fitting linear regression on features of the original predictors for flexible feature representation.
Machine Learning: Features and Model Selection
Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.
Feature Learning: Stability and Curse of Dimensionality
Explores how modern architectures beat the curse of dimensionality and the importance of stability in deep learning models.