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2.1 Divide-and-conquer design paradigm 2.2 Recurrence for merge sort and binary search 2.3 Powering a number 2.4 Fibonacci numbers 2.5 Recursive squaring 2.6 Matrix multiplication 2.7 Strassen's algorithm
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《计算机网络》课程教学资源(学习资料)Teaching Data Communications and Networking to School of Business Undergraduates
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《电子商务 E-business》阅读文献:A recommender system based on genetic algorithm for music data
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广西师范大学出版社:《大数据 The Big Data Revolution》PDF电子书(著:涂子沛)
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To design and use abstract classes (§9.2). To process calendar using the Calendar and GregorianCalendar classes (§9.3). To declare interfaces to model weak inheritance relationships (§9.4). To define a natural order using the Comparable interface (§9.4). To know the similarities and differences between an abstract class and interface (§9.4). To enable objects cloneable using the Cloneable interface (§9.4.4 Optional). To use wrapper classes (Byte, Short, Integer, Long, Float, Double, Character, and Boolean) to wrap primitive data values into objects (§9.5). To create a generic sort method (§9.5). To simplify programming using JDK 1.5 automatic conversion between primitive types and wrapper class types (§9.6)
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⚫ To understand objects and classes and use classes to model objects (§6.2). ⚫ To learn how to declare a class and how to create an object of a class (§6.3). ⚫ To understand the roles of constructors and use constructors to create objects (§6.3). ⚫ To use UML graphical notations to describe classes and objects (§6.3). ⚫ To distinguish between object reference variables and primitive data type variables (§6.4) ...... ⚫ To understand the difference between instance and static variables and methods (§6.10). ⚫ To determine the scope of variables in the context of a class (§6.11). ⚫ To use the keyword this as the reference to the current object that invokes the instance method (§6.12). ⚫ To store and process objects in arrays (§6.13). ⚫ To apply class abstraction to develop software (§6.14). ⚫ To declare inner classes (§6.17)
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《商务智能:数据分析的管理视角 Business Intelligence, Analytics, and Data Science:A Managerial Perspective》教学资源(习题,原书第4版)chapter 8 Future Trends, Privacy and Managerial Considerations in Analytics
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《商务智能:数据分析的管理视角 Business Intelligence, Analytics, and Data Science:A Managerial Perspective》教学资源(习题,原书第4版)chapter 6 Prescriptive Analytics:Optimization and Simulation
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《商务智能:数据分析的管理视角 Business Intelligence, Analytics, and Data Science:A Managerial Perspective》教学资源(习题,原书第4版)chapter 6 Prescriptive Analytics:Optimization and Simulation
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《商务智能:数据分析的管理视角 Business Intelligence, Analytics, and Data Science:A Managerial Perspective》教学资源(习题,原书第4版)chapter 5 Predictive Analytics II:Text, Web, and Social Media Analytics
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