Artificial Intelligence

清華大學計科所
人工智慧
Artificial Intelligence
Robotic AI Lab
ICMS National Tsing Hua University
劉晉良
Jinn-Liang Liu

2020 Fall Course (Seminar)

Machine Learning for Self-Driving Cars

ENG1工一 209, 1:20 – 3:10 (3:20 – 5:20) pm, Wednesdays (2020.9.16 – 2021.1.6)

Send reports to jinnliu@mail.nd.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3.)

Report 1: ppt (demo) by 10/21. Report 2: ppt by 11/25. Report 3: ppt and docx (demo) by 1/6.

Presentations on 12/31 and 1/6. *comma coding

Lecture Notes

l *AI abc: An Introduction to Machine Learning

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

l Convolution in Machine Learning (Convolution)

l Batch Normalization

Part I Supervised Learning

1. A Simple Learning Model: Classification, Target, Hypothesis, Training Data, Learning Algorithm, Weights, Bias, Supervised and Unsupervised Learning

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

3. *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)

4. *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