Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have ...
Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
作者简介
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Dr. Yaser S. Abu-Mostafa is Professor at the California Institute of Technology. His areas of expertise are Machine Learning and Computational Finance. He received his PhD from Caltech where he was awarded the Clauser Prize for the most original doctoral thesis, and later received the Feynman Prize for excellence in teaching. In 2005, the Hertz Foundation established the Abu-Mo...
Dr. Yaser S. Abu-Mostafa is Professor at the California Institute of Technology. His areas of expertise are Machine Learning and Computational Finance. He received his PhD from Caltech where he was awarded the Clauser Prize for the most original doctoral thesis, and later received the Feynman Prize for excellence in teaching. In 2005, the Hertz Foundation established the Abu-Mostafa Fellowship in his honor. He has served as scientific advisor to several corporations and start-up companies in the US and abroad. He has travelled extensively and is fluent in 3 languages.
Malik Magdon Ismail obtained a B.S. in Physics from Yale University in 1993 (summa cum laude, phi beta kappa) and a Masters in Physics (1995) and a PhD in Electrical Engineering with a minor in Physics from the California Institute of Technology in 1998, winning the Wilts prize. He is currently a professor of Computer Science at Rensselaer Polytechnic Institute (RPI), where he is a member of the Theory group. His research interests have included the theory and applications of machine learning, social network algorithms, communication networks and computational finance. In particular, he is interested in the statistical, theoretical and algorithmic aspects of learning from data. He also has consulted in a variety of capacities in computational finance and data mining.
Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and won the outstanding teaching award from the university in 2011. His research interests include theoretical foundations of machine learning, studies on new learning problems, and improvements on learning algorithms. He received the 2012 K.-T. Li Young Researcher Award from the ACM Taipei Chapter, and co-led the team that won the third place of KDDCup 2009 slow track, the champion of KDDCup 2010, and the double-champion of the two tracks in KDDCup 2011.
目录
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Chapter 1. The Learning Problem
Chapter 2. Training versus Testing
Chapter 3. The Linear Model
Chapter 4. Overfitting
Chapter 5. Three Learning Principles
Chapter 1. The Learning Problem
Chapter 2. Training versus Testing
Chapter 3. The Linear Model
Chapter 4. Overfitting
Chapter 5. Three Learning Principles
· · · · · · (收起)
原文摘录
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Using a set of observations to uncover an underlying process (查看原文)
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0 有用 junjie.yao 2014-04-14 13:21:44
besides too concise and short, this is a very good book.
0 有用 Bilionan 2022-03-06 10:03:26
好书，读英文版也不难，老师借鉴的这本书上课，但是讲的没有书好。
0 有用 1984 2022-03-07 21:03:50
真是本好书啊，排版精美，叙述井井有条，从比较偏数学的角度解释了机器学习经典模型。就是自己英语水平太烂。。。没有细读
4 有用 olostin 2017-06-30 21:16:22
一些面试的同学，上来就长篇大论各种算法，特别适合这本书。1.为什么学习有效；2.VC bound&bias var tradeoff；3.overfitting®ularization；4.cross validation；至少要完全懂这四个……
0 有用 qwertyuiop123 2022-04-09 09:27:11
第二章对在没有 distribution shift 的情况下 VC generalization bound 的推导写得非常清楚
0 有用 安德 2024-03-25 16:41:00 四川
Get your VC bound correct; first try the linear model; Paraskavedekatriaphobia.
0 有用 颖鹰 2023-09-28 07:19:03 广东
相当好的机器学习入门书，把整个机器学习的基础理论讲的直白易懂，作者实在太强了！
0 有用 噗赛 2023-09-14 02:21:19 上海
solid thinking frame of “learning” , 配合林軒田老师的“機器學習基石”食用
0 有用 ✟𝕸𝖎𝖘𝖆✟ 2022-11-18 19:51:35 澳大利亚
ETC3555杀我
0 有用 西青 2022-11-15 15:27:37 日本
非常清晰，用来入门ml非常好