Bayesian Theory, Cost Function, Minimization, and Incremental Optimization in Data Assimilation
Explore data assimilation in weather and climate models, covering theory, methods, and applications. Gain hands-on experience with various techniques and their use in atmospheric and oceanic studies.
Ocean Data Assimilation - Lecture 2
Explore advanced ocean data assimilation techniques, their applications in climate modeling, and methods for integrating diverse oceanic observations to improve forecasting accuracy and understanding of marine systems.
Ocean Data Assimilation - Applications in Weather and Climate Models
Explore ocean data assimilation techniques, their applications in monitoring and understanding climate variability, and integration with atmospheric and land surface studies.
Reinforcement Learning
Explore reinforcement learning concepts and their applications in psychology and neuroscience. Hands-on exercises demonstrate how algorithms explain animal learning and dopamine neuron firing.
Makings of a Modern ACID Compliant Distributed Database
Explore the internal design of FaunaDB, focusing on ACID guarantees, distributed systems, and performance optimization techniques like Calvin protocol and control theory applications.
Handling GPS Data with Python
Learn to handle GPS data in Python, from reading/writing tracks to visualizing on OpenStreetMap. Explore libraries, add elevation data, and understand algorithms like Douglas-Peucker and Kalman filters for track simplification and smoothing.
Uncertain Models - Learning from the Past and Optimizing for the Future
Explore ensemble-based methods for handling uncertainty in engineering decisions, focusing on reservoir planning and windmill farm design. Learn about Kalman Filter variants and gradient approximation techniques.
How to Make Money Betting on NBA Using Rocket Science
Explore how Kalman filters, used in rocket science, are applied to NBA betting predictions. Learn about DraftKings' data-driven approach to generate accurate probabilities for bookmaking.
Kalman-Bucy Informed Neural Networks for System Identification
Combining physics-informed neural networks and Kalman filters to identify parameters in continuous-time systems with noisy measurements, improving model precision for complex systems.