Introduction to Deep Learning
Deep Learning is subset of machine learning focused on extracting patterns from data using neural networks and use those patterns to inform the learning tasks.
Learning Controllers - From Engineering to AGI
Exploring deep reinforcement learning for autonomous control systems, from fusion plasma to robotics, with a focus on data-efficient methods for real-world applications.
Deep Learning for Sequences in Quantitative Finance
Overview of deep learning sequence methods in quantitative finance, covering feature extraction, return forecasting, portfolio allocation, and trading execution. Explores applications and challenges in the investment process.
Steps to AGI
Explore DeepMind Alberta's plan for achieving AGI through reinforcement learning, continual learning, and meta-learning in complex environments, with a focus on key steps and core concepts.
Feedback Control from Pixels
Explore feedback control using cameras, bridging control theory and machine learning. Learn about challenges, recent advances, and potential applications in robotics and AI.
AI, Machine Learning and Deep Learning
Explore AI, machine learning, and deep learning concepts, including supervised, unsupervised, and reinforcement learning paradigms. Learn when to use traditional ML or deep neural networks for audio applications.
AI and Robotics: Current Challenges and Future Directions
Explore AI and robotics with Chelsea Finn and Andrew Ng, discussing career paths, current capabilities, challenges, and future prospects in the field. Gain insights into robotics research and learn how to enter this exciting domain.
Stanford Seminar - Decision Transformer: Reinforcement Learning via Sequence Modeling
Innovative approach to reinforcement learning using sequence modeling and Transformer architecture, offering simplicity and scalability while matching or exceeding state-of-the-art performance in various tasks.
Stanford Seminar - Get in Touch: Tactile Perception for Human-Robot Systems
Explore advancements in tactile perception for robots, including sensor technologies, haptic perception, and applications in human-robot interaction and manipulation of deformable objects.
Towards Safe and Efficient Learning in the Physical World - Stanford Seminar
Exploring safe Bayesian optimization and model-based deep reinforcement learning for efficient, safe online learning in real-world applications like robotics and laser tuning.
Underactuated Robotics - Spring 2009
This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods.
The Task Specification Problem
Exploring challenges in AI task specification, focusing on aligning human communication with machine understanding to improve robustness, transfer, and feature learning in AI systems.
Diverse Data and Efficient Algorithms for Robot Learning
Explore diverse data collection and efficient algorithms for robot learning, focusing on large-scale approaches and reinforcement learning for deformable object manipulation.
Data Augmentation for Image-Based Reinforcement Learning
Explores data augmentation techniques for image-based reinforcement learning, presenting a model-free algorithm and a self-supervised framework for visual continuous control tasks, achieving state-of-the-art results.
Tractable Novelty Exploration Over Continuous and Discrete Sequential Decision Problems
Explore cutting-edge width-based planning algorithms for sequential decision problems, covering continuous dynamics, polynomial approximations of novelty, and comparisons with deep reinforcement learning.
Artificial Intelligence by CrashCourse
In 20 episodes, Jabril will teach you about Artificial Intelligence and Machine Learning!
Reinforcement Learning
Explore Q-learning, deep Q networks, and reinforcement learning environments through hands-on tutorials and practical implementations.
Reinforcement Learning Course
Comprehensive exploration of reinforcement learning concepts, from basics to advanced topics, including deep RL, policy gradients, and practical applications in classic games.
Self-Driving Cars With Carla and Python
In this course, you will learn about Carla, an open source autonomous driving simulator. Carla creates a sort of open world with streets, other cars, pedestrians, weather, sensors, and more to help you to write logic to drive self-driving cars, coming wi…
Machine Learning
The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree-based inductive learning, inductive logic programming, reinforcement learning and deep learning through deci…
Evolutionary Conundrums - Cooperation and Traversing Fitness Landscapes
Explore evolutionary biology: cooperation via social norms, fitness landscapes in rapidly evolving populations, and optimal antibiotic use against pathogens using Markov Decision Processes.