Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Reinforcement Learning Fundamentals

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (41)

Deep Learning: Convolutional Networks Log in to Mediaspace to watch this video

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

Introduction to Reinforcement Learning: Concepts and Applications Log in to Mediaspace to watch this video

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

Feed-forward Networks Log in to Mediaspace to watch this video

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Financial Time Series Analysis Log in to Mediaspace to watch this video

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

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

Neural Network Approximation and Learning Log in to Mediaspace to watch this video

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Deep and Convolutional Networks: Generalization and Optimization Log in to Mediaspace to watch this video

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

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

Machine Learning for Feature Extraction Log in to Mediaspace to watch this video

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Optical Computing: All-Analog Photoelectronic Chip for Vision Tasks Log in to Mediaspace to watch this video

Presents an all-analog photoelectronic chip for high-speed vision tasks, addressing challenges in classical computation and proposing a hybrid optical-electrical framework.

Feedback Mechanisms in Visual Intelligence and Adaptation Log in to Mediaspace to watch this video

Explores the significance of feedback mechanisms in visual intelligence and adaptation processes.

Project-Based Learning in Communications and Robotics Log in to Mediaspace to watch this video

Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.

Data-driven Reinforcement Learning Log in to Mediaspace to watch this video

Discusses challenges in AI systems, supervised learning limitations, and the necessity of data-driven methods in reinforcement learning.

Pre-Training: BiLSTM and Transformer Log in to Mediaspace to watch this video

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Neural System Organization Log in to Mediaspace to watch this video

Explores the organization of the nervous system, including neuron structure, synapses, neurotransmitters, and neural circuits.

Supervised Learning: Regression Methods Log in to Mediaspace to watch this video

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Gaussian Naive Bayes & K-NN Log in to Mediaspace to watch this video

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Kernel Methods: SVM and Regression Log in to Mediaspace to watch this video

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Page 2 of 3