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所在平台: Udemy |
课程主页: https://www.udemy.com/course/basic-numerical-methods-numerical-analysis-for-mc-learning/
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课程名称: 机器学习和工程的数值方法基础 课程概述: 数值方法在机器学习、深度学习、人工智能和数据科学中发挥着关键作用。这些方法对于解决这些领域中常见的复杂数学问题至关重要。数值方法的一项重要应用是优化机器学习模型。优化是寻找一组模型参数以最小化给定目标函数的过程，该过程涉及复杂的数学计算，通常需要数值方法。本课程内容结构严谨，涵盖的主题包括： - 有限差分的微积分 - 前向差分与前向差分表 - 反向差分 - 差分算子的属性 - 等间隔插值方法 - 插值方法的假设 - 牛顿-格雷戈里方法/公式 - 牛顿-格雷戈里反向插值公式 - 不等间隔插值 - 拉格朗日插值公式 - 分式差分公式 - 数值微分 - 数值积分 - 一般求积公式 - 平行规则 - 辛普森一三分之一规则 - 辛普森三八分之三规则 - 维德尔规则 - 代数方程和超越方程的数值解法 - 代数方程的性质 - 合成除法 - 使用合成除法找多项式导数 - 方程求根的方法：图形法、二分法、假位置法、迭代法、牛顿-拉夫森法 数值方法也用于大数据集的分析。数据科学家经常遇到传统方法无法处理的大型数据集，此时可以使用随机线性代数和蒙特卡罗模拟等数值方法高效处理数据。本课程还提供问答支持，并根据学生反馈不断更新，未来计划添加新的主题。 所以，为什么还要犹豫呢？今天就报名，迈出实现目标的第一步。借助正确的工具和支持，您可以实现梦想，取得优异成绩。不要错过这个提升自信、出类拔萃的机会。
Numerical methods play a critical role in machine learning, deep learning, artificial intelligence, and data science. These methods are essential for solving complex mathematical problems that are common in these fields.One of the most important uses of numerical methods in these areas is in the optimization of machine learning models. Optimization is the process of finding the set of model parameters that minimize a given objective function. This process involves complex mathematical calculations that often require numerical methods.Here, the course is thoughtfully structured and organised. The topics covered are-The Calculus of Finite DifferencesThe Forward DifferencesForward Difference TableThe backward DifferencesProperties of Difference OperatorInterpolation with equal IntervalsAssumptions for methods of InterpolationNewton Gregory Method/FormulaNewton Gregory Formula for backward InterpolationInterpolation with unequal IntervalsLagrange's Interpolation FormulaDivided Difference FormulaNumerical DifferentiationNumerical IntegrationGeneral Quadrature FormulaTrapezoidal RuleSimpson's One Third (1/3) RuleSimpson's Three Eighths(3/8)RuleWeddle's RuleNumerical Solution of Algebraic and Transcendental EquationProperties of Algebraic EquationsSynthetic DivisionDerivative of a Polynomial with synthetic divisionMethods of finding out roots of equation: Graphical MethodBisection MethodRegula Falsi Method/False Position MethodIteration MethodNewton Raphson MethodNumerical methods are also used in the analysis of large datasets. Data scientists often encounter datasets that are too large to be processed using traditional methods. In these cases, numerical methods such as randomized linear algebra and Monte Carlo simulations can be used to efficiently process the data.Here , in this course you'll receive support through a Q & A section, and the course is continually updated based on student feedback, with plans to add new topics in the future.So why wait?Enroll today and take the first step toward achieving your goals. With the right tools and support, you can make your dreams a reality and achieve the high score you deserve. Don't miss out on this opportunity to excel and boost your confidence.
