EE-411: Fundamentals of inference and learning

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Lectures in this course (30)

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Linear Models: Ridge, OLS and LASSO

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Singular Value Decomposition: Ridge and LASSO

Covers the concept of Singular Value Decomposition and its application in regression techniques.

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.

SVMs and Feature Maps

Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.

Feature Maps and Kernels

Covers feature maps, Representer theorem, kernels, and RKHS in machine learning.

Mercer Theorem and Kernels

Explores the Mercer Theorem, Kernels, and their role in machine learning applications.

Neural Networks: Two-layer Networks and Backpropagation

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.

Crash course on Deep Learning

Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.

Deep Learning Fundamentals

Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.

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