Lecture

Metrics for Classification

Graph Chat

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.

Related lectures (64)

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Signals & Systems I: Micro-Systems and Communication Systems

Introduces the fundamentals of signals and systems, communication systems, and signal processing.

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Entropy and Sampling Theory

Explores entropy, exponential family, sampling theory, and statistical inference from samples.

Machine Learning Review

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Ensemble Methods: Random Forests

Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.

Introduction to Sampling

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

Signal Processing Fundamentals

Covers the basics of signal processing and electric circuits.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

Linear Models: Basics

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Signals, Instruments, and Systems

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

3, STM / AFM 2024

Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

2024 Lecture 3 PHYS-467

Signal Sampling: Bandwidth and Spectrum

Introduces signals, frequencies, bandwidth, filtering, and sampling in signal processing.

Playing Connect Four

Explores the implementation of Connect Four game functionalities and testing methods.