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Back to course
Mathematics for Engineer and Data Scientist
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 5-4-Kalman 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 12-1 - Recurrent Neural Networks and Transformers
Lecture 12 - 2 - Convolutional Neural Networks
Lecture 12 - 3 - Deep Generative Modeling
Lecture 12-4 - Reinforcement Learning
Lecture 12-5 - Deep Learning New Frontiers
Lecture 12-6 - LiDAR for Autonomous Driving
Lecture 12-7 - Automatic Speech Recognition
Lecture 12-8 - 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