Machine learning

Related lectures (32)

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

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

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

Covers the perceptron model and backpropagation algorithm in neural networks.

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

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.