No.	Date	Contents	Reference	Video
1	2021/09/22	Welcome and Course Introduction​	ML Syllabus 2021.pdf ​ML Syllabus 2021_anno.pdf​ ML Course Introduction 2021_dist.pdf ​ML Course Introduction 2021_anno.pdf​​	2021 Fall Introduction to Machine Learning and Deep Learning W1L1​ 2021 Fall Introduction to Machine Learning and Deep Learning W1L2​​ ​2021 Fall Introduction to Machine Learning and Deep Learning W1L3
2	2021/09/29	Fundamental of Machine Learning (I)​	20210922 Fundamentals of Machine Learning (I)_dist.pdf​ Boolean_Learning_Example_dist.pdf Boolean_Learning_Example_anno.pdf Boolean_Learning.ipynb Boolean_Learning_Analytical.ipynb Boolean_Learning_Feasible.ipynb	2021 Fall Introduction to Machine Learning and Deep Learning W2L1 2021 Fall Introduction to Machine Learning and Deep Learning W2L2 2021 Fall Introduction to Machine Learning and Deep Learning W2L3
3	2021/10/06	Fundamental of Machine Learning (II), (III)​	20210929 Fundamentals of Machine Learning (II)_dist.pdf 20210929 Fundamentals of Machine Learning (II)_anno.pdf Introducing_Scikit-Learn_dist.pdf Bias_Variance.pdf Bias_Variance_anno.pdf Bias_variance.ipynb	​2021 Fall Introduction to Machine Learning and Deep Learning W3L1 2021 Fall Introduction to Machine Learning and Deep Learning W3L2 2021 Fall Introduction to Machine Learning and Deep Learning W3L3
4	2021/10/13	Fundamental of Machine Learning (III)​	20211006 Fundamentals of Machine Learning (III)_dist.pdf 20211006 Fundamentals of Machine Learning (III)_anno.pdf Cross_validation.ipynb pima-indians-diabetes.data.csv​ ​Classical Machine Learning: Classification and Regression (I) 20211013 ML Classification and Regression (I)_dist.pdf 20211013 ML Classification and Regression (I)_anno.pdf Knowing_Your_Data.pdf Decision_Tree.pdf Data_understand.ipynb Data_prepare.ipynb Decision_tree_tutorial.ipynb Decision_tree_overfit.ipynb	2021 Fall Introduction to Machine Learning and Deep Learning W4L1 2021 Fall Introduction to Machine Learning and Deep Learning W4L3
5	2021/10/20	​Seminar on Artificial Intelligence for Engineering Applications - U-Net	Classical Machine Learning: Classification and Regression (II) 20211020 ML Classification and Regression (II)_dist.pdf 20211020 ML Classification and Regression (II)_anno1.pdf 20211020 ML Classification and Regression (II)_anno2.pdf Support_Vector_Machine.pdf Support_Vector_Machine_anno.pdf Ensemble_Rationale.pdf Ensemble_Rationale_anno.pdf Ensemble_Bagging.pdf Ensemble_Bagging_anno.pdf Ensemble_Random_Forest.pdf Ensemble_Random_Forest_anno.pdf SVM_tutorial.ipynb Base_Classfiers.ipynb MajorityVote.ipynb Bagging.ipynb RFDigitsRecog.ipynb RFFeatures.ipynb	2021 Fall Introduction to Machine Learning and Deep Learning W5L1 2021 Fall Introduction to Machine Learning and Deep Learning W5L2&3
6	2021/10/27	​Seminar on Artificial Intelligence for Engineering Applications - GraphSage	Hamilton, W. L., Ying, R., & Leskovec, J. (2017, December). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 1025-1035). ​A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark,  Material Video	​Link
7	2021/11/03	ValueSeminar on Artificial Intelligence for Engineering Applications - Masked Autoencoders Are Scalable Vision Learners	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., & Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2021). Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377. JinWon Lee (2021). PR-355: Masked Autoencoders Are Scalable Vision Learners. Retrieved from youtube  DeepReader (2021). MAE: Masked Autoencoders Are Scalable Vision Learners. Retrieved from youtube Jia-Yau Shiau (2021). Masked Autoencoders: 借鏡BERT與ViT的Self-Supervised Learners. Retrieved from youtube	Link
8	2021/11/10
9	2021/11/17
10	2021/11/24
11	2021/12/01
12	2021/12/08
13	2021/12/15
14	2021/12/22
15	2021/12/29
16	2022/01/05