Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
PAC Learning: Empirical Risk Minimization
Graph Chatbot
Related lectures (49)
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Unsupervised Learning: Dimensionality Reduction
Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Active Learning for Molecular Design
Covers machine learning approaches for material design, practical examples, and software tools for research.
World of Data: Machine Learning and Value Chain
Delves into machine learning, data insights, and commercial value in the digital world.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Machine Learning Fundamentals
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Supervised Learning with kNN: Regression Model
Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.
Machine Learning Fundamentals
Covers key concepts and examples of machine learning algorithms and techniques.
Gaussian Naive Bayes & K-NN
Log in to Mediaspace to watch this video
Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.
Statistical Learning: Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Machine Learning Fundamentals
Log in to Mediaspace to watch this video
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Neural Network Approximation and Learning
Log in to Mediaspace to watch this video
Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.
Deep Learning: Dimensionality and Data Representation
Log in to Mediaspace to watch this video
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
Supervised Learning: Classification Algorithms
Log in to Mediaspace to watch this video
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Supervised Learning Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Machine Learning: Supervised and Unsupervised Learning Techniques
Log in to Mediaspace to watch this video
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Classification Algorithms: Generative and Discriminative Approaches
Log in to Mediaspace to watch this video
Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Decision Tree Classification
Log in to Mediaspace to watch this video
Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.
Financial Time Series Analysis
Log in to Mediaspace to watch this video
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Previous
Page 1 of 3
Next