EE-411 Fundamentals of inference and learning
EE-411 Fundamentals of inference and learning
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14a, Crash course on ensemble methods: Bagging…
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14b, Reinforcement learning
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13b, Crash course on generative models
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13a, Gaussian Mixture Clustering:…
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12b, Denoising, Auto-encoder, Gaussian mixture…
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11b, Unsupervised learning: Young-Eckart-Mirsky…
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11a, Conclusions on statistical learning theory
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10b, Crash course on statistical learning theory
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10a, conclusions on deep learning and intro to…
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9b, Crash course on Deep Learning
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9a, Crash course on Deep Learning
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8b, Two-layer Neural Networks and Backpropagation
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8a, Mercer Theorem, Kernels and Speed-up
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7b, Feature maps, Representer theorem, Kernels,…
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