關於 machine learning linear model ，我們在網路上蒐集到這些相關的討論、資訊與評價

🔥 "อยากสร้างคอมพิวเตอร์ที่เรียนรู้ได้ด้วยตัวเอง ! อยากทำหุ่นยนต์แบบในหนังไซไฟ ต้องรู้ Machine Learning" !!
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ต้องบอกว่างานด้าน AI จะเป็นหนึ่งในงานที่สำคัญมาก ๆ ในอนาคต ที่จะให้คอมพิวเตอร์คอยคิด ตรวจหา ทำงานแทนเราได้นั่นเอง
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📌 ซึ่งไม่ว่างานเล็ก ๆ อย่างกิจกรรมประจำวัน จนถึง การวิเคราะห์ชิ้นเนื้อเพื่อหาเซลล์มะเร็ง และ ค้นหาความเปลี่ยนแปลงที่เกิดขึ้นในอวกาศ ในอนาคตก็จะมีศาสตร์ด้าน Machine Learning อยู่ในนั้นทั้งสิ้น
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วันนี้แอดได้รวมความรู้ด้านคณิตศาสตร์ สถิติที่สำคัญของคนที่สนใจอยากทำ Machine Learning มาฝากกันดังนี้ <3
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1. Linear Algebra
2. Analytic Geometry
3. Matrix Decomposition
4. Vector Calculus
5. Probability & Distributions
6. Continuous Optimization
7. Linear Regression
8. Principal Component Analysis
9. Mixture model
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ซึ่งแน่นอนว่าบางตัวหลายคนอาจจะเจอระหว่างเรียนมหาวิทยาลัยมาแล้ว แต่ถ้าใครอยากทบทวน แอดมีหนังสือมาแจกด้วยแหละ (ถูกลิขสิทธิ์ด้วยนะ !)
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กับหนังสือ Mathematics for Machine Learning ใครสนใจคลิกได้เลยย >> https://mml-book.github.io/book/mml-book.pdf
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และ สุดท้ายนี้ต้องบอกว่านี่เป็นแค่ส่วนหนึ่งเท่านั้น เพราะสาย ML นี้มีอะไรให้เรียนรู้ตั้งหลายด้านทั้ง Programming, Algorithm หรือ เรื่องอื่น ๆ ที่จำเป็นในคณิตศาสตร์ สถิติ ซึ่งถ้าใครต้องการต่อยอด
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แอดว่าหนังสือเล่มนี้ที่แจกน่าจะช่วยให้เราเริ่มต้นได้ กับ พื้นฐานที่ดีจ้าาา :D
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#borntoDev - 🦖 สร้างการเรียนรู้ที่ดีสำหรับสายไอทีในทุกวัน

【演講】2019/11/19 (二) @工四816 (智易空間)，邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan） 演講「Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management」

IBM中心特別邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan）前來為我們演講，歡迎有興趣的老師與同學報名參加！

演講標題：Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management
演 講 者：Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間：2019/11/19(二) 9:00 ~ 12:00
地 點：交大工程四館816 (智易空間)
活動報名網址：https://forms.gle/vUr3kYBDB2vvKtca6

報名方式：
費用：（費用含講義、午餐及茶水）
1.費用：(1) 校內學生免費，校外學生300元/人 (2) 業界人士與老師1500/人
2.人數：60人，依完成報名順序錄取（完成繳費者始完成報名程序）

※報名及繳費方式：
1.報名：請至報名網址填寫資料
2.繳費：
（1）親至交大工程四館813室完成繳費(前來繳費者請先致電)
（2）匯款資訊如下：
戶名: 曾紫玲（國泰世華銀行 竹科分行013）
帳號: 075506235774 (國泰世華銀行 竹科分行013）
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據

聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw

Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.

2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.

3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.

Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.

Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).

【時間序列量化交易 - FinLab實體活動】

機器學習模型用於實際交易，不容易！

FinLab在此次的研討會中，介紹了很多小撇步，讓機器學習效果更好，更能夠應用於實際交易。以下是重點摘要：

📕【Labeling】 是很容易被大家忽略的部分，以「持有N天後報酬率」當做label真的好嗎？
我們翻閱 paper 列舉更好的方法，

Tripple barrier:
[Prado 2018] Advances in Financial Machine Learning
Continous trading signal

Continuous trading signals
[Dash 2016] A hybrid stock trading framework integrating technical analysis with machine learning techniques

Trading Point decision
[Chang 2009] Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction

📕【 CNN神經網路】除了做出好的label，我們也介紹了CNN要如何實際用於trading：[Sezer 2018] 產生很多不同參數的技術指標，並且將這些技術指標做成圖片，來預測交易訊號，非常的有趣！
[Sezer 2018] Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach

📕【 Feature importance】通常我們會利用 feature importance來篩選重要的feaature，但是我們平常真的做對了嗎？這部分我就先賣個關子XD

研討會頭影片：
https://drive.google.com/file/d/18dEAalouKvWAtlZSUjc8X9_i8uOHMwci/view?usp=sharing

假如你看到這裡，恭喜你！告訴你一個小秘密，就是我們要辦一場教學活動，由於是第一次實驗性質試辦，此活動酌收茶點費100元，設備場地感謝Fugle的贊助！

當天是個三個小時的小課程，會介紹如何用上述的內容做出一個有效的機器模型策略喔！（需要自行帶筆電）

時間：7/27號 （星期六） 下午 2 點到 5 點
地點：台北捷運西門站五號出口3分鐘路程
名額：25名（白老鼠XD）
金額：100元用來吃吃喝喝（因為是白老鼠，才是這個價格XD）
條件：完成AI股票理專課程的「第 1 章單元 2」
（課程網址：https://hahow.in/cr/finlab-ml）

報名辦法：
這個星期天(7/14)晚上10點準時收看finlab粉絲團，我們會在此時發佈表單，採先搶先贏的方式報名喔！（場地空間有限～不好意思囉）