Machine Learning: Theory and Practice (機器學習：理論與實踐)
Kalita, Jugal
- 出版商: CRC
- 出版日期: 2024-12-19
- 售價: $2,310
- 貴賓價: 9.5 折 $2,195
- 語言: 英文
- 頁數: 282
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367433524
- ISBN-13: 9780367433529
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相關分類:
Machine Learning
尚未上市，無法訂購
相關主題
商品描述
Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples.
Features:
- Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own.
- Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration
- Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods.
This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.
商品描述(中文翻譯)
《機器學習：理論與實踐》提供了機器學習中最受歡迎方法的介紹。這本書涵蓋了回歸分析（包括正則化）、基於樹的方法（包括隨機森林和提升樹）、人工神經網絡（包括卷積神經網絡 CNN）、強化學習以及專注於聚類的無監督學習。各主題以概念性方式介紹，並附上必要的數學細節。解釋清晰，並以圖示和範例輔助說明。對於每種討論的機器學習方法，書中提供了相應的 R 程式語言庫及程式範例。
特色：
- 以易於閱讀的方式呈現常用的機器學習演算法，適合高年級本科生或初學研究生，以及希望自學機器學習的數學和/或程式設計導向的個人。
- 涵蓋所討論的機器學習演算法的數學細節，以確保深入理解，並促進進一步探索。
- 提供適當的程式範例，確保對機器學習方法的概念、理論和實踐理解。
本書主要旨在向高年級本科生和初學研究生介紹機器學習的基本主題。主題數量故意保持在較小範圍，以便在一個學期或一個季度內涵蓋所有內容。這些主題深入探討，並在短時間內的教學限制內進行。因此，本書能夠提供基礎，讓學生能夠閱讀進階書籍和研究論文。
作者簡介
Dr. Jugal Kalita teaches Computer Science at the University of Colorado, Colorado Springs, where he has been a professor since 1990. He received M.S. and Ph.D. degrees in Computer and Information Science from the University of Pennsylvania in Philadelphia in 1988 and 1990, respectively. Prior to that, he had received an M.Sc. in Computational Science from the University of Saskatchewan in Saskatoon, Canada in 1984; and a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur in 1982.
Dr. Jugal Kalita's expertise is in the areas of Artificial Intelligence and Machine Learning, and the application of techniques in Machine Learning to Natural Language Processing, Network Security, and Bioinformatics. At the University of Colorado, Colorado Springs, and Tezpur University, Assam, India, where he is an adjunct professor, Dr. Kalita has supervised 15 Ph.D. and 125 M.S. students to graduation, and has mentored 100 undergraduates in independent research. He has published 250 papers in journals and refereed conferences, including prestigious conferences such as International Conference on Machine Learning (ICML), Association for Advancement of Artificial Intelligence (AAAI), North American Chapter of the Association for Computational Linguistics (NAACL), International Conference on Computational Linguistics (COLING) and Empirical Methods in Natural Language Processing (EMNLP). Dr. Kalita is the author of On Perl: Perl for Students and Professionals, Universal Press, 2003. He is also a co-author of Network Anomaly Detection: A Machine Learning Perspective, CRC Press, 2013; DDOS Attacks: Evolution, Detection, Prevention, Reaction and Tolerance, CRC Press, 2016; Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools, Springer Nature, 2017; and Gene Expression Data Analysis, A Statistical and Machine Learning Perspective, CRC Press, 2021.
Dr. Kalita has received several teaching, research and service awards at the University of Colorado, Colorado Springs, in the Department of Computer Science, and the College of Engineering and Applied Science. He received the prestigious Chancellor's Award at the University of Colorado, Colorado Springs, in 2011, in recognition of lifelong excellence in teaching, research and service. More details about Dr. Kalita can be found at http: //www.cs.uccs.edu/ kalita.
作者簡介(中文翻譯)
Dr. Jugal Kalita 在科羅拉多大學科羅拉多泉校區教授計算機科學，自1990年以來一直擔任教授。他於1988年和1990年分別在賓夕法尼亞大學獲得計算機與資訊科學的碩士和博士學位。在此之前，他於1984年在加拿大薩斯喀徹溫省的薩斯喀徹溫大學獲得計算科學的碩士學位，以及於1982年在印度理工學院卡哈爾古爾獲得計算機科學與工程的學士學位。
Dr. Jugal Kalita 的專長領域包括人工智慧和機器學習，以及將機器學習技術應用於自然語言處理、網路安全和生物資訊學。在科羅拉多大學科羅拉多泉校區及印度阿薩姆邦的特茲普爾大學（他是該校的兼任教授），Kalita 博士指導了15位博士生和125位碩士生順利畢業，並指導了100名本科生進行獨立研究。他在期刊和經過審核的會議上發表了250篇論文，包括如《國際機器學習會議》（ICML）、《人工智慧促進協會》（AAAI）、《北美計算語言學協會》（NAACL）、《國際計算語言學會議》（COLING）和《自然語言處理中的實證方法》（EMNLP）等知名會議。Kalita 博士是《On Perl: Perl for Students and Professionals》的作者，該書由Universal Press於2003年出版。他也是《Network Anomaly Detection: A Machine Learning Perspective》（CRC Press, 2013）、《DDOS Attacks: Evolution, Detection, Prevention, Reaction and Tolerance》（CRC Press, 2016）、《Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools》（Springer Nature, 2017）以及《Gene Expression Data Analysis, A Statistical and Machine Learning Perspective》（CRC Press, 2021）的共同作者。
Kalita 博士在科羅拉多大學科羅拉多泉校區的計算機科學系和工程與應用科學學院獲得了多項教學、研究和服務獎項。他於2011年獲得科羅拉多大學科羅拉多泉校區的校長獎，以表彰他在教學、研究和服務方面的終身卓越表現。關於Kalita博士的更多詳細資訊，請參見 http://www.cs.uccs.edu/kalita。
