1:21:49Lecture 10: Bayes Filter
A 82-minute video from dLabRoboticsMIT: “Lecture 10: Bayes Filter”.
dLabRoboticsMIT
3.21K subscribers on YouTube
Shorts, livestreams, trailers and promos are never listed. Unless a curator approved this channel in full, only individually picked videos appear.
1:21:49
1:26:57Lecture 11: Simultaneous Localization and Mapping (SLAM)
A 87-minute video from dLabRoboticsMIT: “Lecture 11: Simultaneous Localization and Mapping (SLAM)”.
1:20:02Lecture 12: Particle Filter
A 80-minute video from dLabRoboticsMIT: “Lecture 12: Particle Filter”.
1:29:21Lecture 13: Non Parametric Linear System Identification
A 89-minute video from dLabRoboticsMIT: “Lecture 13: Non Parametric Linear System Identification”.
1:22:02Lecture 14: Parametric Linear System Identification
A 82-minute video from dLabRoboticsMIT: “Lecture 14: Parametric Linear System Identification”.
1:22:51Lecture 15: Asymptotic Distribution of Parameter Estimates
A 83-minute video from dLabRoboticsMIT: “Lecture 15: Asymptotic Distribution of Parameter Estimates”.
1:23:19Lecture 16: Unbiased Identification
A 83-minute video from dLabRoboticsMIT: “Lecture 16: Unbiased Identification”.
1:25:58Lecture 17: Subspace Methods for System Identification
A 86-minute video from dLabRoboticsMIT: “Lecture 17: Subspace Methods for System Identification”.
1:29:51Lecture 18: MOESP and N4SID
A 90-minute video from dLabRoboticsMIT: “Lecture 18: MOESP and N4SID”.
1:24:50Lecture 19: Nonlinear Function Approximation
A 85-minute video from dLabRoboticsMIT: “Lecture 19: Nonlinear Function Approximation”.
1:27:05Lecture 1: Introduction to Identification, Estimation, and Learning
A 87-minute video from dLabRoboticsMIT: “Lecture 1: Introduction to Identification, Estimation, and Learning”.
1:25:11Lecture 20: Neural Networks and Error Backpropagation
A 85-minute video from dLabRoboticsMIT: “Lecture 20: Neural Networks and Error Backpropagation”.
1:33:24Lecture 21: Deep Learning with CNN and RNN
A 93-minute video from dLabRoboticsMIT: “Lecture 21: Deep Learning with CNN and RNN”.
1:02:42Lecture 22: Support Vector Machines and Kernel Methods
A 63-minute video from dLabRoboticsMIT: “Lecture 22: Support Vector Machines and Kernel Methods”.
1:24:12Lecture 23: Gaussian Processes
A 84-minute video from dLabRoboticsMIT: “Lecture 23: Gaussian Processes”.
1:21:28Lecture 25 Dual Faceted Linearization with Application to Nonlinear Model Predictive Control
A 81-minute video from dLabRoboticsMIT: “Lecture 25 Dual Faceted Linearization with Application to Nonlinear Model Predictive Control”.
1:18:19Lecture 2: Least Squares
A 78-minute video from dLabRoboticsMIT: “Lecture 2: Least Squares”.
1:23:18Lecture 3: Recursive Least Squares with Forgetting Factor
A 83-minute video from dLabRoboticsMIT: “Lecture 3: Recursive Least Squares with Forgetting Factor”.
1:30:58Lecture 4: Random Variables and Random Processes
A 91-minute video from dLabRoboticsMIT: “Lecture 4: Random Variables and Random Processes”.
1:24:12Lecture 5: Principal Component Regression
A 84-minute video from dLabRoboticsMIT: “Lecture 5: Principal Component Regression”.
1:24:08Lecture 6: Partial Least Squares Regression
A 84-minute video from dLabRoboticsMIT: “Lecture 6: Partial Least Squares Regression”.
1:32:11Lecture 7: Discrete Time Kalman Filter
A 92-minute video from dLabRoboticsMIT: “Lecture 7: Discrete Time Kalman Filter”.
1:38:03Lecture 8: Continuous Time Kalman Filter
A 98-minute video from dLabRoboticsMIT: “Lecture 8: Continuous Time Kalman Filter”.
1:22:06Lecture 9: Extended Kalman Filter and Unscented Kalman Filter
A 82-minute video from dLabRoboticsMIT: “Lecture 9: Extended Kalman Filter and Unscented Kalman Filter”.