Lecture

Machine Learning: Supervised and Unsupervised Learning Techniques

Related lectures (35)
Linear Regression Essentials
Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Machine Learning Fundamentals: Regularization and Cross-validation
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Nonlinear ML Algorithms
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Error Decomposition and Regression Methods
Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.
Data-Driven Modeling: Regression
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Birth Month Impact on Athlete Success
Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.
Linear Models: Basics
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Feature Engineering: Missing Data and Standardization
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.

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.