Lecture

Image Classification: Decision Trees & Random Forests

Related lectures (32)

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Decision Trees: Induction & Attributes

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Land Use Mapping in the Alps

Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Recurrent Neural Networks: Language Detection

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Data Driven Science: MODNet Methodology

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Feature Extraction & Clustering Methods

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.