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The Connection Between the Exponential Distribution and the Poisson Process
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New Certificate and Masters Degree in Machine Learning and Artificial Intelligence for Engineering!!
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The Hazard Rate and Memoryless Property of the Exponential Distribution
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The Exponential Distribution: Time Between Poisson Events
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The Geometric Distribution: The First Success of a Bernoulli Distribution
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The Poisson Distribution: The Rare Event Limit of a Binomial Distribution
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The Standard Unit Normal and Probability Computations
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The Normal Distribution: The Limit of Binomial Distribution for Large "n"
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TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
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Bernoulli and Binomial Random Variables
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Random Variables and Probability Distributions
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Independence in Probability
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Bayes' Theorem Example: Drug Testing 🌿
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Bayes' Theorem (with Example!)
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The Law of Total Probability
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Conditional Probabilities
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The Binomial Distribution and the Multinomial Distribution
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Quality Control, Non-Destructive Inspection, and the Multinomial Distribution
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Mode Sensitivity for Fluid Flows via Lagrangian Coherent Structures
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The Birthday Problem in Probability: P(A) = 1 - P(not A)
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Set Theory in Probability: Sample Spaces and Events
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Counting Probabilities with Combinatorics and the Factorial
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Gentle Introduction to Probability: Counting Coin Flips and Dice
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Probability and Statistics: Overview
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Python Symbolic Regression (PySR) [Physics Informed Machine Learning]
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Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
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Fourier Neural Operator (FNO) [Physics Informed Machine Learning]
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Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]
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Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]
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Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]
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Neural Network Control in Collimator 2.0 & New Educational Videos!!!
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Residual Networks (ResNet) [Physics Informed Machine Learning]
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Neural ODEs (NODEs) [Physics Informed Machine Learning]
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Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]
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SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
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AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]
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AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]
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Using sparse trajectory data to find Lagrangian Coherent Structures (LCS) in fluid flows
32:35
AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]
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The Future of Model Based Engineering: Collimator 2.0
23:12
AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]
36:30
AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]
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AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]
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AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]
47:27
Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering
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Can we make commercial aircraft faster? Mitigating transonic buffet with porous trailing edges
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A Neural Network Primer
11:46
Supervised & Unsupervised Machine Learning
13:48
A Machine Learning Primer: How to Build an ML Model
25:23
Arousal as a universal embedding for spatiotemporal brain dynamics
28:02
New Advances in Artificial Intelligence and Machine Learning
14:51
Nonlinear parametric models of viscoelastic fluid flows with SINDy
14:16
[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]
10:19
[8/8] Control for Societal-Scale Challenges: Road Map 2030 [Recommendations]
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[2/8] Control for Societal-Scale Challenges: Road Map 2030 [Societal Drivers]
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[1/8] Control for Societal-Scale Challenges: Road Map 2030 [Introduction]
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[6/8] Control for Societal-Scale Challenges: Road Map 2030 [Education]
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[3/8] Control for Societal-Scale Challenges: Road Map 2030 [Technological Trends]
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[7/8] Control for Societal-Scale Challenges: Road Map 2030 [Ethics, Fairness, & Regulatory Issues]
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[4/8] Control for Societal-Scale Challenges: Road Map 2030 [Emerging Methodologies]
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