Lectures related to Reinforcement Learning: Non-Stationary Policies and OPPO

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Neural Networks: Training and Optimization

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.