科技英文寫作

臺北科技大學 114-2 學期課程，課號 359993。

課程大綱

This semester's Deep Learning (DL) course will focus on Machine Learning (ML), DL, and Generative AI in data science analytics. We will look at supervised, unsupervised and structured learning methods, which will include classic Multi-Layer Perceptron (MLP) as a shallow or Deep Neural Network (DNN), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), k-Means clustering, and Generative Adversarial Networks (GANs). This course is designed for graduate students who are interested in AI and its applications in data science analytics. Students who take this course should have mathematical maturity of undergraduate curricula in science and engineering, including Calculus, Discrete Mathematics, Linear Algebra and programming language such as C/C++/MATLAB/Python. Teaching approaches conducted in this course emphasize analysis, modeling and design principles of AI methodology. Case studies, engineering applications of AI in data science analytics, will also be covered in this course. Collaboration Policy: You are free to form study groups and discuss programming assignments and projects. However, you must write up assignments and code independently, and you must acknowledge in your submission all the students you discussed with.

課程進度

本課程將「科技英文寫作」融入於「深度學習」教學（中文授課、英文教材）。課程係以學習「深度學習」專業知識技術為主，而以學習「科技英文」英文知識為輔！！ Week Topic (PART I) 1 課程介紹 2 八大詞性與五大句型 3 連接詞與關係代名詞 (PART II) 4 Foundations for AI 5 Supervised learning (I) -- Multi-Layer Perceptron (MLP) (classification problems) (scikit-learn) 6 Supervised learning (II) -- Multi-Layer Perceptron (MLP) (non-linear regression/function approximation problems) (Programming assignment #1) 7 Supervised learning (III) -- MLP (TensorFlow/Keras) 8 Programming assignment #1 (Due / Oral presentation) **9 Mid-term paper reading report (Due / Oral presentation) 10 Supervised learning (IV) -- MLP (time-series data modeling and forecasting) (scikit-learn/TensorFlow) 11 Unsupervised learning -- k-Means clustering (scikit-learn) (PART III) 12 Supervised learning (V) -- MLP as Deep Learning models (TensorFlow (GPU accelerated)) 13 Programming assignment #2 (Due / Oral presentation) 14 Supervised learning (VI) -- Convolutional Neural Networks (CNNs)/Long Short-Term Memory (LSTM) (TensorFlow/Keras (GPU accelerated)) (Programming assignment #2) 15 Supervised/Structured learning (I) -- Generative Adversarial Networks (Generative AI) 16 Programming assignment #2 (Due / Oral presentation) 17 Supervised/Structured learning (II) -- Multi-Task Learning/Transfer Learning **18 Final paper reading report (Due / Oral presentation)

評量方式

- 學習總成績: 85起算 - Assignments (PPT only): 至多 +5 (Optional) (Two or Three programming assignments: No assignment turned in late will be accepted) - Mid-term paper reading report (PPT only): 至多 +5 (Optional) - Final paper reading report (PPT only): 至多 +5 (Optional)

教材

【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 Course lectures will be distributed through our University's iStudy+ (e-learning) system.