Concept

Active learning (machine learning)

Related lectures (25)

Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Sensorimotor Contingency: Amir Zamir

By Amir Zamir explores sensorimotor contingency, intelligence without representation, curriculum learning, and machine learning strategies.

Automating Scanning Probe Microscopy: Techniques and Applications

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Machine Learning in Human Rights Documentation

Discusses the application of machine learning in human rights documentation and the challenges and benefits it entails.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Machine Learning in Human Rights: HURIDOCS

Explores machine learning in human rights, focusing on defining goals, handling false positives and negatives, and ensuring transparency and trust.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Support Vector Regression: Kernel Tricks

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Biases, ML performance and adversarial ML threats

Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Graph Machine Learning

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.

Word Embeddings: Context and Representation

Explores word embeddings, emphasizing word-context relationships and low-dimensional representations.

Quantum Machine Learning: Theory and Applications

Explores quantum machine learning, representations of molecules, kernel regression, and the interplay between physics and machine learning.

Analyzing Hebbian Learning Rule

Explores rate-based Hebbian learning, covariance rules, and weight vector growth in neural networks.