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所在平台: Udemy |
课程主页: https://www.udemy.com/course/understanding-computational-problem-algorithms-with-java/
课程评论：没有评论
Coursera课程：理解计算问题与Java算法 **课程概述：** 本课程旨在解决学习者在缺乏清晰指导的情况下，急于直接编程而忽略编程原理和概念理解的问题。课程提出一种“概念与情境模型”，引导学习者深入理解编程原理和算法设计。 该模型将世界问题抽象为计算问题，并将其视为在多维空间中随时间尺度变化的向量。问题的维度代表其复杂度，从简单问题（存在于子空间）到复杂问题（存在于整个空间）。例如，简单问题可能表现为一维直线上的点，而复杂问题则涉及二维、三维甚至更高维度（确定性系统）或无限维度（非确定性系统）。 正是这种问题的维度差异，催生了对集合（sets）、列表（lists）、字典（dictionaries）、数组（arrays）等不同数据结构的需求，以便在不同维度上有效地解决计算问题，这在许多机器学习算法中尤为明显。例如，解决高维度复杂问题可能需要多维数组，而一维数组则足以解决低维度问题。对于复杂问题，可能需要数组的数组（array of arrays），而简单问题则可以通过基本的变量和原始数据类型来处理，无需大量或复杂的数据结构。 **核心价值：** 通过这个模型，学习者可以培养一种思考和推理能力，从而有效地设计和实现算法来解决现实世界中的计算问题。课程提供了理解力和实践性的指导，帮助学习者建立扎实的编程基础。
While we understand the concepts and context of programming languages, we often tend to encourage ourselves and other learners to dive into programming straightaway without understanding the programming principles well. This may be so due to lack of a directive system or a way to understand the principles or concepts well. This paper, however, tends to provide a guiding system for learners to learn programming or algorithm well by following a model known as "a conceptual and contextual model" for understanding the world problems as being a computational problem affecting the algorithm design and implementation. This model aims to provide improvement in understanding the concept or principle of programming practices and applications. The model models the world problems in terms of vectors in the space that scale up with time in different dimensions. The dimension spectrum indicates the complexity of the problems that scale up from simple problems in the subspace to the complex problems in the whole space with time. The simple problem is modelled to be in the subspace whereas the complex ones are modelled to be in the whole space. For example, smaller problems may be found at a point, 1D….in a straight line and complex problems are found in the 2D, 3D, up to some n for a deterministic system or to the infinity ∞ for non-deterministic system. The spectrums of the problems are the reason for the need of data structure such as sets, lists, dictionary, arrays to solve the computational in different dimension as done in some of the machine learning algorithms. For example, multidimensional array may be required as solve complex problem in the whole space as opposed to single dimensional arrays that may be required to solve problems in lower dimensions. Array of arrays may be needed to solve complex problems, whereas small problems may be manipulated and solved by using other primitives and variables without the need of many or single sets of data. This is how this model may help us think and reason out how design and implement algorithm to solve the world computational problems.
