清華大學動力機械工程學系

人工智慧

Artificial Intelligence

PME National Tsing Hua University

劉晉良
Jinn-Liang Liu

2024 Fall Course: Introduction to Artificial Intelligence
113上學期課程: 人工智慧

*OP Coding

Lecture Notes

*AI abc: An Introduction to Machine Learning

*Gradient Descent and Backpropagation in Machine Learning (Automatic Differentiation: Forward & Reverse Modes, Jacobian)

*Convolution in Machine Learning (Convolution)

*Batch Normalization

*RNN Language Models (Attention and Transformer)

Part I   Supervised Learning

1.   *Google Tutorial for ML Beginners: Image Recognition, MNIST, Softmax Regression (92%), Cross Entropy, Gradient Descent, Back Propagation, Computation Graph (TF mnist 1.0)

2.   *Tensorflow and Deep Learning I (by Martin Gorner): Deep Learning (98%), ReLU, Learning Rate, Overfitting, Dropout (98.2%), Convolutional Neural Network (CNN, 99.3%)  (TF mnist 3.1)

3.   *Tensorflow and Deep Learning II (by Martin Gorner) (RNN1): Batch Normalization (99.5%) (TF mnist 4.2), MNIST Record (Kaggle: 100%), Recurrent Neural Network, Deep RNN, Long Short Term Memory, Gated Recurrent Network

Part II   Self-Driving Cars

1.   Introduction to Self-Driving Cars

Carnegie Mellon U 1989,  CMU Vehicle, Computer

comma.ai openpilot 2018, commaai, comma-GitHub, openpilot

Tesla Autopilot 2019, auto vs open

Self-Driving Car, Autonomous Car

2.   Data: comma2k19, comma10k, Cityscapes, Apollo,

3.   How to ensure the safety of Self-Driving Cars

Past Courses

2024 Spring Course: Mobile Robots and Self-Driving Cars (移動機器人與自駕車)
2024 openpilot八堂課
2023 Fall Course: Introduction to Artificial Intelligence (人工智慧)