Lectures related to Applied Machine Learning

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Applied Machine Learning: Features and Models

Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.

Data Driven Science: MODNet Methodology

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

Landscape and Generalisation in Deep Learning

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

Machine Learning Fundamentals

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

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

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.

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Image Classification: Decision Trees & Random Forests Log in to Mediaspace to watch this video

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Supervised Learning: Regression Methods Log in to Mediaspace to watch this video

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

Introduction to Machine Learning Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

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.

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.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

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

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.