Lectures related to Policy Gradient Evaluation: Example (1-step horizon)

Linear Classification: Signed Distance and Perceptron

Explores signed distance, perceptron, logistic regression, cross entropy, and multi-class classification.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Introduction: classification by a simple perceptron

Covers the concept of a simple perceptron and its ability to solve linearly separable problems by imposing a separating hyperplane.

Linear Binary Classification: Perceptron, SGD, Fisher's LDA

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Neural Networks: Perceptron and Backpropagation

Covers the basics of neural networks, including the perceptron model and backpropagation.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Neural Networks: Perceptron

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Deep Learning Paradigm

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

Neural Networks: Basics and History

Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.

Perceptron: Part 2

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Dimensionality Reduction

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Statistical Physics of Learning

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Policy Gradient Methods: Reinforcement Learning Techniques

Provides an overview of policy gradient methods in reinforcement learning, focusing on the log-likelihood trick and the transition from batch to online learning.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Kernel Methods: Nonlinear Separation Surfaces

Explores kernel methods for nonlinear separation surfaces using polynomial and Gaussian kernels in Perceptron and SVM algorithms.