Alexander Amini Courses

Recurrent Neural Networks and Transformers

Comprehensive exploration of recurrent neural networks, LSTMs, and attention mechanisms in deep learning, covering theory, implementation, and applications for sequence modeling tasks.

• YouTube
• 58 minutes
• On-Demand
• Free Video

MIT Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 49 minutes
• On-Demand
• Free Video

AI in Healthcare

Explore AI applications in healthcare, from cancer screening to genomics, addressing challenges like bias and model limitations for improved patient care and equitable outcomes.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Towards AI for 3D Content Creation

Explore AI-driven 3D content creation, from synthesizing worlds to neural simulation. Learn cutting-edge techniques for generating, composing, and rendering 3D objects and scenes using deep learning.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Taming Dataset Bias via Domain Adaptation

Explore techniques to address dataset bias in deep learning, including adversarial domain alignment, pixel space alignment, and consistency enforcement for improved model generalization.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Deep CPCFG for Information Extraction

Explore deep learning techniques for information extraction using Conditional Probabilistic Context-Free Grammars. Learn about document parsing, schema representation, and handling noisy data in this advanced lecture.

• YouTube
• 41 minutes
• On-Demand
• Free Video

AI Bias and Fairness

Comprehensive exploration of AI bias and fairness, covering types, sources, and mitigation strategies. Examines interpretation-driven and data-driven biases, feature bias, and advanced debiasing techniques for ethical AI development.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Evidential Deep Learning and Uncertainty

Explore evidential deep learning and uncertainty estimation in neural networks, covering probabilistic learning, types of uncertainty, Bayesian methods, and practical applications.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Deep Learning New Frontiers

Explore deep learning limitations and new frontiers, including expressivity, generalization, failure modes, uncertainty, adversarial attacks, and emerging areas like graph learning and AutoML.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Reinforcement Learning

Comprehensive exploration of deep reinforcement learning, covering Q-functions, policy gradients, and real-world applications like AlphaGo. Ideal for understanding RL fundamentals and advanced concepts.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Convolutional Neural Networks

Explore convolutional neural networks for computer vision, covering feature extraction, convolution operations, and applications like object detection and self-driving cars.

• YouTube
• 56 minutes
• On-Demand
• Free Video

MIT 6.S191 - Recurrent Neural Networks

Comprehensive lecture on Recurrent Neural Networks, covering theory, implementation, and applications. Explores sequence modeling, LSTM, attention mechanisms, and practical examples in deep learning.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Machine Learning for Scent

Explore machine learning applications in digitizing scent, from understanding olfactory processes to predicting odor descriptors using graph neural networks and interpreting molecular fragrance data.

• YouTube
• 39 minutes
• On-Demand
• Free Video

MIT: Neural Rendering

Explore neural rendering techniques, from forward and inverse rendering to 3D data representations, with insights on RenderNet, neural point-based graphics, and HoloGAN in this comprehensive lecture.

• YouTube
• 37 minutes
• On-Demand
• Free Video