
Slides

Lecture 1  First Order Differential Equation

Lecture 2  Gaussian Noise & Brownian Motion

Lecture 3  Stochastic Differential Equation Part 1

Lecture 3  Stochastic Differential Eq Part 2

Lecture 4  Probability, Conditional Probability and Random Variable.

Lecture 5  Kalman Filter Theory and Application

Lecture 5  Kalman Filter

Review Stochastic model and Kalman Filter

Kalman filter  part 3

Lecture 54Kalman filter  part 4

Lecture 5  Optimization 1 (unconstrained)

Lecture 7  Linear Algebra 1 (least square)

Lecture 8  Optimization 2 (constrained) part 1

Lecture 8  Optimization 2 (constrained) part 2

Introduction to Machine Learning

Lecture 9  Classical Machine Learning

Lecture 9  Classical Machine Learning part 2

Introduction to Machine Learning  Practice

Lecture 10  Neural Network

Lecture 11  Convolutional Neural Network

Lecture 12  Introduction to Deep Learning

Lecture 121  Recurrent Neural Networks and Transformers

Lecture 12  2  Convolutional Neural Networks

Lecture 12  3  Deep Generative Modeling

Lecture 124  Reinforcement Learning

Lecture 125  Deep Learning New Frontiers

Lecture 126  LiDAR for Autonomous Driving

Lecture 127  Automatic Speech Recognition

Lecture 128  AI for Science


Slide A

Lecture 1  First Order Differential Equation

Lecture 2  Gaussian Noise & Brownian Motion

Lecture 3  Stochastic Differential Equation Part 2

Lecture4  Probability, Conditional Probability and Random Variable.

Lecture5  Kalman Filter

Lecture 5  Optimization 1 (unconstrained)

Lecture 7  Linear Algebra 1 (least square)

Lecture 8  Optimization 2 (constrained) part 1

Lecture 8  Optimization 2 (constrained) part 2

Lecture 10  Neural Network

Introduction to Machine Learning

Lecture 7  Linear Algebra 1 (least square)
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