Lectures related to Introduction to Machine Learning

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Linear Regression: Introduction and Training

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised 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.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Introduction to Machine Learning: Course Overview and Basics Log in to Mediaspace to watch this video

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Machine Learning Basics Log in to Mediaspace to watch this video

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Linear Regression: Foundations and Applications Log in to Mediaspace to watch this video

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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.

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.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

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

Introduction to Data Science Log in to Mediaspace to watch this video

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

Machine Learning Basics Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.