This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the ’black box’ approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline’s robustness and credibility.While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for a broad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the ’how,’ this text provides answers to the ’why’ questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality.Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints, offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses, this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.

• 关于产品：
• ● 正版保障：本网站隶属于中国国际图书贸易集团公司，确保所有图书都是100%正版。
• ● 环保纸张：进口图书大多使用的都是环保轻型张，颜色偏黄，重量比较轻。
• ● 毛边版：即书翻页的地方，故意做成了参差不齐的样子，一般为精装版，更具收藏价值。
• 
  关于退换货：
• 由于预订产品的特殊性，采购订单正式发订后，买方不得无故取消全部或部分产品的订购。
• 由于进口图书的特殊性，发生以下情况的，请直接拒收货物，由快递返回：
• ● 外包装破损/发错货/少发货/图书外观破损/图书配件不全（例如：光盘等）
  并请在工作日通过电话400-008-1110联系我们。
• 签收后，如发生以下情况，请在签收后的5个工作日内联系客服办理退换货：
• ● 缺页/错页/错印/脱线
• 
  关于发货时间：
• 一般情况下：
• ●【现货】 下单后48小时内由北京（库房）发出快递。
• ●【预订】【预售】下单后国外发货，到货时间预计5-8周左右，店铺默认中通快递，如需顺丰快递邮费到付。
• ● 需要开具发票的客户，发货时间可能在上述基础上再延后1-2个工作日（紧急发票需求，请联系010-68433105/3213）；
• ● 如遇其他特殊原因，对发货时间有影响的，我们会第一时间在网站公告，敬请留意。
• 
  关于到货时间：
• 由于进口图书入境入库后，都是委托第三方快递发货，所以我们只能保证在规定时间内发出，但无法为您保证确切的到货时间。
• ● 主要城市一般2-4天
• ● 偏远地区一般4-7天
• 
  关于接听咨询电话的时间：
• 010-68433105/3213正常接听咨询电话的时间为：周一至周五上午8：30～下午5：00，周六、日及法定节假日休息，将无法接听来电，敬请谅解。
• 其它时间您也可以通过邮件联系我们：customer@readgo.cn,工作日会优先处理。
• 
  关于快递：
• ● 已付款订单：主要由中通、宅急送负责派送，订单进度查询请拨打010-68433105/3213。