BMES 5475-0

Brief

This course will cover basic concepts of machine learing, deep learning and their applications.

Description

Course keywords: Machine Learning, Deep Learning, AI, Python 一、課程說明(Course Description) This is introductory course of deep learning and its applications to biomedical imaging. 二、指定用書(Text Books) Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning. MIT Press, 2016. (http://www.deeplearningbook.org/) 三、參考書籍(References) 1. Daniel A. Roberts, Sho Yaida, Boris Hanin, The Principles of Deep Learning Theory, Cambridge University Press, 2022. (https://arxiv.org/abs/2106.10165) 2. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006. (https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book) 3. Keras. https://keras.io/ 4. PyTorch. https://pytorch.org/ 5. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4-th Ed., Pearson, 2020. 6. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 2018. 7. Haykin, Simon, Neural Networks and Learning Machines, 3rd. Ed., Pearson, 2016. 四、教學方式(Teaching Method) Lecture 五、教學進度(Syllabus) 1. Introduction, Python, Keras (PyTorch), Anaconda, Google Colab. 2. Perceptron and Neural Networks. 3. Convolutional Neural Networks (CNNs). 4. Descent Optimization Algorithms, Normalzations. 5. Notable CNNs: LeNet, AlexNet, VGG 16 + 19, GoogleNet 6. object Detection: R-CNN 7. Residual CNN 8. Autoencoder, Generative Adversarial Networks 9. Denoising 10. Segmentation Using U-NET 11. Transformers (Attention Is All You Need) 六、成績考核(Evaluation) 1. Homeworks 40% 2. Project 60% 七、可連結之網頁位址 https://eeclass.nthu.edu.tw/course/bulletin/12001

Timetable

Scores

Average Percentage 90.36

Std. Deviation 2.28

Remarks

本課程為16週課程

Restrictions

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization