Lecture

Dimensionality Reduction: Curse of Dimensionality

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)

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation

Explores over-learning, generalization, and under-learning in machine learning models.

How to Lie with Statistics

Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.

Lecture 04 (2024)

Dependence Measures: Rank Correlations

Covers rank correlations, tail dependence, and copula fitting methods.

Advanced Machine Learning: Fundamentals and Applications

Covers the fundamentals of advanced machine learning, emphasizing practical applications through interactive exercises and projects.

Portfolio Management: Risk and Return

Explores portfolio rate of return, valuation, risk characterization, and historical performance, emphasizing diversification benefits and mean-variance analysis.

Euclidean Correlation Functions

Explores Euclidean correlation functions and the transition to real time using Wick rotation, along with a discussion on good and bad habits.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Autocorrelation and Periodicity

Explores autocorrelation, periodicity, and spurious correlations in time series data, emphasizing the importance of understanding underlying processes and cautioning against misinterpretation.

Model Assessment: Metrics and Selection

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Signal Approximation and Orthogonal Bases

Explores signal approximation, orthogonal bases, Fourier series, correlation, and vector geometry.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Discrete Panel Data

Explores discrete panel data, covering static and dynamic models with panel effects and their practical implications.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Regression Analysis: Interpretation and Applications

Explores multiple regression, ANOVA, interpretation of coefficients, and regression estimation.

A Localization Tale: Practical Course by BIOP

Covers practical aspects of using Fiji for everyday tasks in biological imaging.

Support Vector Machines: Non-Linear Data Mapping

Explores mapping non-linear data to higher dimensions using SVM and covers polynomial feature expansion, regularization, noise implications, and curve-fitting methods.

Stylized Facts: Reproducible Research

Covers stylized facts in finance and reproducible research in scientific computing.