智慧軟體研究模組
Special Topics and Labs on Intelligent Software
| 節 | 週二 | 週四 |
|---|---|---|
2 09:00–09:50 | 智慧軟體研究模組 CM218 3 節連堂 | |
3 10:10–11:00 | ||
4 11:10–12:00 | ||
5 13:20–14:10 | 智慧軟體研究模組 CM218 2 節連堂 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
Unit 1_智慧農業(林勻蔚):介紹目前農業相關議題與使用物聯網技術之解決辦法 Unit 2_儲能系統應用與規劃(黃建中): 我國未來綠能發電技術採用多元能源及再生能源,譬如太陽光電與風力發電技術等間歇能源,因此未來分散式的發電與電力有效運用須要搭配各式儲能技術。教學目標包含:1.介紹世界能源與再生能源技術;2.使學生熟悉儲能技術的應用背景與儲能技術種類、特性及應用領域;3.使學生了解儲能技術市場分析與儲能技術應用國際範例,學習儲能用於生活的基本認知與知識。 Unit 3_Mathemetics for Machine Learning(馬清文): This course is design to provides on some essential mathematical foundations on which machine learning and artificial intelligence are based. Topics include 1) algebra foundations such as equations, functions, and graphs; 2) differential calculus such as derivatives and optimization; 3) linear algebra such as vector-matrix multiplication and matrix decomposition and; 4) statistics, probability and basic information theory. We will also write python codes to explore the concepts and technologies that are introduced in this course. Unit 4_Deep Learning Hardware and Software(馬清文): Deep learning algorithms require heavy computations. To deal with the demand, judiciously design hardware and software are built. In this course, we will talk about popular software platforms such as Tensorflow/keras and Pytorch. We explore the basic difference between these software platforms and develop the idea of how to choose one from them for our own works. Regarding the deep learning hardware, we will take a look at deep leaning accelerators which are used in edge AI and well-known among many AI chip startups. Unit 5_智慧感測(林勻蔚): 本課程將介紹如何蒐集環境資訊、記錄與追蹤,之後可做為大數據分析之用途
Unit 1_智慧農業(林勻蔚):Python程式撰寫能力 Unit 2_儲能系統應用與規劃(黃建中):N/A Unit 3_Mathemetics for Machine Learning(馬清文): 1. Computer programming skill, especially Python and C. 2. College linear algebra, calculus and probability concepts. Unit 4_Deep Learning Hardware and Software(馬清文): 1. Computer programming skill, especially writing Python and C programs. 2. Computer architecture concepts such as CPU, instruction set, memory, data bus, etc. Unit 5_智慧感測(林勻蔚):Python初階能力、簡易電路製作
Unit 1_智慧農業(林勻蔚):課程有助教可解答學生疑難問題。 Unit 2_儲能系統應用與規劃(黃建中):課堂講授、投影片 Unit 3_Mathemetics for Machine Learning(馬清文): Web resource: Essential math for machine learning: https://www.edx.org/course/essential-math-for-machine-learning-python-editi-2 Unit 4_Deep Learning Hardware and Software(馬清文): 1. Stanford CS231 2020 lecture slides: http://cs231n.stanford.edu/slides/2020/lecture_6.pdf 2. Stanford CS231 2017 Youtube video: Lecture 8 | Deep Learning Software: https://www.youtube.com/watch?v=6SlgtELqOWc 3. nVidia Deep learning accelerator: http://nvdla.org/ 4. NVDLA Deep Learning Inference Compiler is Now Open Source: https://devblogs.nvidia.com/nvdla/ Unit 5_智慧感測(林勻蔚):課程有助教可解答學生疑難問題。
Unit 1_智慧農業(林勻蔚):以每一課堂單元作業進行評分。 Unit 2_儲能系統應用與規劃(黃建中): 作業成績 50%,小測驗 50% 作業內容:國內智慧儲能應用概念發想與軟硬體技術整合應用規劃 Unit 3_Mathemetics for Machine Learning(馬清文):Homework: 100% Unit 4_Deep Learning Hardware and Software(馬清文):Homework: 100% Unit 5_智慧感測(林勻蔚):以每一課堂單元作業進行評分。
| 週次 | 主題 |
|---|---|
| 第 1 週 | 課程說明(9/3(二)9:00-12:00) |
| 第 2 週 | Unit 1_智慧農業(林勻蔚):介紹智慧農業相關議題 |
| 第 3 週 | 放假 |
| 第 4 週 | Unit 1_智慧農業(林勻蔚):介紹智慧農業之數據資訊紀錄與分析 |
| 第 5 週 | Unit 1_智慧農業(林勻蔚):介紹智慧農業疾病預測 |
| 第 6 週 | Unit 2_儲能系統應用與規劃(黃建中): 單元主題:國際儲能政策與綠能技術進展 課程內容:國際間對於能源需求不斷擴大,就全球能源產業而言,未來再生能源占比將逐年上升,近期因戰爭及能源短缺等影響,促使各國持續調高再生能源發電比例,加上全球倡議綠色新能源、碳中和議題之下,根據國際能源署(IEA)研究指出,2022-2027年再生能源將增加達2400GW。而儲能系統具有發電、輸/配電、穩定電網之功能,在結合再生能源後,得以支持公用、工/商業領域等多元化能源技術與服務等應用,具有可穩定再生能源間歇性及靈活調配能源的優勢。 |
| 第 7 週 | Unit 2_儲能系統應用與規劃(黃建中): 單元主題:台灣儲能技術與產業發展 課程內容:台灣在儲能相關技術上,最早自不斷電系統相關產品切入,逐漸擴大到工業用定置型電源、不同場域用電管理與防災備援用途等產品線,晚近也從國內電網用場域示範運行、再生能源搭配儲能系統上投入,逐步累積產品與技術實力。 |
| 第 8 週 | Unit 2_儲能系統應用與規劃(黃建中): 單元主題:儲能系統設計應用規劃與健康度智慧評估技術 課程內容:因應國內再生能源及電網需求,政府積極推動電網端及發電端儲能設備布建,預計2022年併聯100MW,2025年目標達1,500MW以上,成長快速。國內MW等級儲能系統以鋰電池為主,尚缺乏健康檢測及評估技術,為有效監控儲能系統運轉即時狀況,需投入儲能電池健康檢測技術,掌握系統維運週期及排程,提升儲能系統可靠度與運轉效益。 |
| 第 9 週 | Unit 3_Mathemetics for Machine Learning(馬清文) : 1. algebra foundations such as equations, functions, and graphs; 2. differential calculus such as derivatives and optimization; |
| 第 10 週 | Unit 3_Mathemetics for Machine Learning(馬清文) : linear algebra such as vector-matrix multiplication and matrix decomposition; |
| 第 11 週 | Unit 3_Mathemetics for Machine Learning(馬清文) : statistics, probability and basic information theory. |
| 第 12 週 | Unit 4_Deep Learning Hardware and Software(馬清文): Part 1: Machine Learning Software Platform We will focus on the comparison of Pytorch and Tensorflow 1.x. |
| 第 13 週 | Unit 4_Deep Learning Hardware and Software(馬清文): Part 2: Tensorflow Lite vs. Darknet We explore two methods of deploying deep neural network onto edge devices and mobile devices. |
| 第 14 週 | Unit 4_Deep Learning Hardware and Software(馬清文): Part 3: nvidia deep learning accelerator We will see the design of a popular deep learning accelerator which is well known among many AI chip startups. |
| 第 15 週 | Unit 5_智慧感測(林勻蔚): 介紹感測相關技術、環境數據分析 |
| 第 16 週 | Unit 5_智慧感測(林勻蔚): 介紹微氣象站功能、硬體架構、電路設計 |
| 第 17 週 | Unit 5_智慧感測(林勻蔚): 介紹圖形化資訊顯示介面、資料紀錄、資料分析比較 |
| 第 18 週 | 期末專題(1/2(四)9:00-12:00) |
Unit 1_智慧農業(林勻蔚):N/A Unit 2_儲能系統應用與規劃(黃建中): 儲能技術概論 (第2版) 作者:曾重仁/ 張仍奎/ 陳清祺/ 薛康琳/ 江沅晉/ 李達生/ 翁芳柏/ 林柏廷/ 李岱洲/ 謝錦隆 出版社:全華圖書股份有限公司 出版日期:2022/10/11 Unit 3_Mathemetics for Machine Learning(馬清文): 1. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. MATHEMATICS FOR MACHINE LEARNING. Cambridge University Press, 2020 2. Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. Chapter 2,3,4,5. MIT Press, 2016 Unit 4_Deep Learning Hardware and Software(馬清文):N/A Unit 5_智慧感測(林勻蔚):N/A