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Lecture
Machine Learning Biases
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Related lectures (31)
Biases, ML performance and adversarial ML threats
Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Machine Learning: Supervised and Unsupervised Learning Techniques
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
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.
Regression Trees and Ensemble Methods in Machine Learning
Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Introduction to Machine Learning
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Introduction to Image Classification
Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Machine Learning Review
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Machine Learning Biases
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Introduction to Machine Learning: Course Overview and Basics
Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Machine Learning at the Atomic Scale
Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.
Gradient Descent and Linear Regression
Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Nearest Neighbor Rules: Part 2
Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.
Statistical Learning: Fundamentals
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Image Classification: Decision Trees & Random Forests
Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Binary Classification by Regression: Decision Functions and Cost Functions
Explores binary classification by regression, decision functions, and various cost functions.
Linear Regression: Basics
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
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