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From Nicolas Flammarion
Guest Lecture - Edouard Grave (Kyutai Lab) Audio and Speech Language Models -
From Martin Jaggi
Generative Models: GANs and Diffusion Models -
From Martin Jaggi
LLMs and Self-Supervised Learning -
From Martin Jaggi
Large Language Models -
From Martin Jaggi
Text Representation Learning -
From Martin Jaggi
Matrix Factorizations and Recommender Systems -
From Nicolas Flammarion
Unsupervised Learning, K-Means and GMM -
From Nicolas Flammarion
Ethics and Fairness in ML -
From Nicolas Flammarion
Adversarial Machine Learning -
From Nicolas Flammarion
Neural Networks: Convolutional Nets, Regularization, Data augmentation, Dropout -
From Nicolas Flammarion
Neural Networks, Training, SGD and Backpropagation -
From Nicolas Flammarion
Neural Networks: Basic Structure, Representation Power -
From Nicolas Flammarion
Kernel Ridge Regression and the Kernel trick
Machine learning and other data analysis tools are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning and artificial intelligence will be introduced, analyzed and practically implemented.
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