Rule-based machine learning

Related lectures (31)

Page 3 of 4

Explores bagging as a regularization method in deep learning, training multiple model variants on different data subsets to improve generalization.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores transformers, emphasizing self-attention and MLP mechanisms for efficient sequence processing.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Covers fundamental principles, opportunities, and challenges in machine learning.

Introduces financial markets, time series, machine learning applications in finance, and natural language processing.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Explores chemical environment representations, symmetrized correlations, and machine learning applications at the atomic scale.