Lecture
Introduction to Machine Learning
Related lectures (54)
Introduction to Machine Learning
Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.
Introduction to Image Classification
Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Supervised Learning Overview
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Unsupervised Learning: Clustering
Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
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.
Clustering: k-means
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Machine Learning Fundamentals
Covers key concepts and examples of machine learning algorithms and techniques.
Recurrent Neural Networks: Language Detection
Explores language detection using Recurrent Neural Networks and supervised learning concepts.
Classification: Introduction
Covers clustering and classification, building models to assign objects to classes based on attribute values.