MO833 AI-BASED MOBILITY DESIGN

CLASS MATERIALS

Lecture Slides: KLMS

REFERENCE VIDEOS

Youtube (In Korean): Link

CODE

Prepare
1) This class uses Google Colab for practice. If you prefer, you can install Jupyter Notebook and TensorFlow on your personal computer. See below for how to prepare.
TensorFlow + Colab + Python
2) This class uses bracket data, which is structural design data. Please download below.
data_3000, data_1000

Convolutional Neural Network (CNN)

1) Colab
  --MNIST: CNN_Sequential, CNN_Functional
  --Bracket: CNN_Sequential

2) Jupyter Notebook
  --MNIST: CNN_Sequential, CNN_Functional
  --Bracket: CNN_Sequential

# Compare learning methods (MNIST)
Sequential+ModelFit / Sequential+GradientTape /
Functional+ModelFit / Functional+GradientTape /
Subclassing+ModelFit / Subclassing+GradientTape
Ensemble

Autoencoder (AE) & Anomaly Detection

1) Colab
  --Bracket: Autoencoder, Anomaly Detection

2) Jupyter Notebook
  --Bracket: Autoencoder, Anomaly Detection

# Compare learning methods (Bracket)
ModelFit / GradientTape

Variational AutoEncoder (VAE)

1) Colab
  --Bracket: VAE

2) Jupyter Notebook
  --Bracket: VAE

# Compare learning methods (Bracket)
ModelFit / GradientTape

Generative Adversarial Network (GAN)

1) Colab
  --Bracket: GAN

2) Jupyter Notebook
  --Bracket: GAN

# Compare learning methods (Bracket)
ModelFit / GradientTape

Advanced Models

1) Convolutional Autoencoder (CAE)
  --Bracket: ModelFit / GradientTape

2) Deep Convolutional GAN (DCGAN)
  --MNIST: Keras

3) Conditional GAN (cGAN)
  --MNIST: Keras

4) Boundary Equilibrium GAN (BEGAN)
  --MNIST: Keras

Machine Learning Models

0. Preprocessing
1) Colab: CODE
2) Jupyter Notebook: CODE

1. Logistic Regression: CODE

2. Support Vector Machine (SVM): CODE

3. Decision Tree & Random Forest: CODE