●教學方式(Teaching Method)
<br>投影片授課 / 實例示範
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<br>● 教學進度(Syllabus)
<br>Week 1: Basics of Machine Learning
<br>Week 2: Artificial Neural Network and backpropagation
<br>Week 3: Deep learning and CNN
<br>Week 4: CNN Model Training
<br>Week 5: Deep Learning software fr<x>ameworks
<br>Week 6: CNN Architectures
<br>Week 7: Sample Applications with CNNs
<br>Week 8: Generative Adversarial Networks (GAN)
<br>Week 9: Sample Applications with GAN models
<br>Week 10: Recurrent Neural Network and LSTM
<br>Week 11: Midterm Exam
<br>Week 12: Deep Reinforcement Learning
<br>Week 13: Practical model training techniques
<br>Week 14: Anomaly detection
<br>Week 15: Domain Adaptation
<br>Week 16: Face recognition 
<br>Week 17: AOI with deep learning
<br>Week 18: Final project presentation
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<br>● 成績考核(Evaluation)
<br>隨堂測驗、作業、期末專題