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MACHINE LEARNING IS GOING TO DEFINE THE NEXT ERA; PEOPLE SAY IT WILL HAVE SIMILAR EFFECTS AS ELECTRICITY OR INTERNET HAD IN their TIMES .ARITIFICIAL INTELLIGENCE IS THE NEXT BEST THING IN THE WORLD OF TECHNOLOGY , BLOCKCHAIN AFTER THAT
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This document provides an introduction to MATLAB. It covers MATLAB basics like arithmetic, variables, vectors, matrices and built-in functions. It also discusses plotting graphs, programming in MATLAB by creating functions and scripts, and solving systems of linear equations. The document is compiled by Endalkachew Teshome from the Department of Mathematics at AMU for teaching MATLAB.
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This document discusses optimization techniques in MATLAB. It describes how to perform both unconstrained and constrained optimization. For unconstrained problems, the fminunc function is used to find the minimum of an objective function. For constrained problems, fmincon is used to minimize an objective function subject to inequality, equality, and bound constraints. The document provides an example of using these functions to solve a sample constrained optimization problem.
A machine learning method for efficient design optimization in nano-optics
The document describes a machine learning method for efficient design optimization in nano-optics using Gaussian process regression and Bayesian optimization. It discusses how Gaussian process regression can be used to build regression models from expensive black-box functions to enable model-based optimization and integration. Bayesian optimization is then used to iteratively query the black-box function at points of maximum expected improvement to find its minimum. The method can incorporate derivative observations to speed up optimization by providing additional training data for the Gaussian process. Differential evolution is also utilized to efficiently maximize the expected improvement at each iteration. The approach is demonstrated on benchmark optimization problems, showing it outperforms other algorithms like L-BFGS and particle swarm optimization.
Mit6 094 iap10_lec03
Here are the steps to solve this ODE problem:
1. Define the ODE function:
function dydt = odefun(t,y)
dydt = -t.*y/10;
end
2. Solve the ODE:
[t,y] = ode45(@odefun,[0 10],10);
3. Plot the result:
plot(t,y)
xlabel('t')
ylabel('y(t)')
This uses ode45 to solve the ODE dy/dt = -t*y/10 on the interval [0,10] with initial condition y(0)=10.
A MATLAB project on LCR circuits
This document discusses using MATLAB to solve differential equations related to electric circuits. It begins by explaining some advantages of MATLAB, such as its use of matrices, vectorized operations, and graphical output capabilities. It then provides an example of using MATLAB to solve the first order differential equation iR+Ldi/dt=E(t), which models an LCR circuit. The document also discusses solving second order differential equations manually and with MATLAB code. It provides an example of solving the second order equation d2q/dt2+10dq/dt+250q=0 both manually and using MATLAB code.
Remainder theorem
The document discusses the Remainder Theorem, which provides a way to factorize polynomials by dividing them by factors and obtaining a remainder. There are two methods for finding the remainder: long division/evaluation and synthetic division. Evaluation involves substituting the factor value into the polynomial, while synthetic division arranges the coefficients and repeatedly multiplies and adds down the line. The document provides examples of using both methods and notes that synthetic division allows determining the full quotient polynomial.
Unit 2 in daa
1) The document describes the divide-and-conquer algorithm design paradigm. It can be applied to problems where the input can be divided into smaller subproblems, the subproblems can be solved independently, and the solutions combined to solve the original problem.
2) Binary search is provided as an example divide-and-conquer algorithm. It works by recursively dividing the search space in half and only searching the subspace containing the target value.
3) Finding the maximum and minimum elements in an array is also solved using divide-and-conquer. The array is divided into two halves, the max/min found for each subarray, and the overall max/min determined by comparing the subsolutions.
algorithm Unit 2
1) The document describes the divide-and-conquer algorithm design paradigm. It splits problems into smaller subproblems, solves the subproblems recursively, and then combines the solutions to solve the original problem.
2) Binary search is provided as an example algorithm that uses divide-and-conquer. It divides the search space in half at each step to quickly determine if an element is present.
3) Finding the maximum and minimum elements in an array is another problem solved using divide-and-conquer. It recursively finds the max and min of halves of the array and combines the results.
bobok
This document provides an overview of MATLAB, including:
- MATLAB is a software package for numerical computation, originally designed for linear algebra problems using matrices. It has since expanded to include other scientific computations.
- MATLAB treats all variables as matrices and supports various matrix operations like addition, multiplication, element-wise operations, and matrix manipulation functions.
- MATLAB allows plotting of 2D and 3D graphics, importing/exporting of data from files and Excel, and includes flow control statements like if/else, for loops, and while loops to structure code execution.
- Efficient MATLAB programming involves using built-in functions instead of custom functions, preallocating arrays, and avoiding nested loops where possible through matrix operations.
Similar to ML ALL in one (1).pdf (20)
Solving Optimization Problems using the Matlab Optimization.docx
Solving Optimization Problems using the Matlab Optimization.docx
The following ppt is about principal component analysis
The following ppt is about principal component analysis
A machine learning method for efficient design optimization in nano-optics
A machine learning method for efficient design optimization in nano-optics
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Charging Fueling & Infrastructure (CFI) Program Resources by Cat Plein
Cat Plein, Development & Communications Director of Forth, gave this presentation at the Forth and Electrification Coalition CFI Grant Program - Overview and Technical Assistance webinar on June 12, 2024.
定制(london学位证书)英国伦敦大学毕业证本科学历原版一模一样
原版定制【微信:bwp0011】《(london学位证书)英国伦敦大学毕业证本科学历》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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美洲杯押注靠谱的软件成立于1934年，隶属于美洲杯押注靠谱的软件酒店管理集团，有着八十多年的历史，有“博彩公司代言人”的美誉，是全球最具影响的博彩公司之一。美洲杯押注靠谱的软件在英国经营着1500多家投注站，全球客户达30万之众，是全球最大的电话投注服务公司。美洲杯押注靠谱的软件除了直接接收玩家的投注外，还接受其他博彩公司的风险转注，起着博彩业“保险公司”的作用。有玩过线上博彩平台的玩家们，一定对美洲杯押注靠谱的软件绝不陌生。
The last lesson in comic form for English art integrated project class 12
English art integrated project class 12 for Ch 1 The Last lesson
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【网址🎉ac44.net🎉】欧洲杯竞猜是一家创立于1998年的在线的体育博彩公司。欧洲杯竞猜由库拉索岛政府授权，可以从事体育博彩相关活动。成立初期，欧洲杯竞猜专注于快速发展的国内市场，现在它为全世界的玩家和职业玩家提供投注服务。毫无疑问，欧洲杯竞猜是欧洲乃至全球最受欢迎的知名在线体育博彩公司。玩家的积极反馈、存款和提款的安全性都充分证明了该公司的可靠性。欧洲杯竞猜 Sports公司 以其良好的服务而得到了客户的认可。该公司在2004年获得了最高水平服务奖、2006年最佳博彩公司奖（EOG.com）、最佳博彩运营商前5名（The Online Wire）和著名门户网站“Gambling”颁发的最佳优惠和奖金奖项，这是对这家博彩公司的最为中肯的评价。
按照学校原版(UniSA文凭证书)南澳大学毕业证快速办理
咨询办理【(UniSA毕业证书)南澳大学毕业证】【176555708微信号】未毕业成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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The last lesson in comic form for English art integrated project class 12
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ML ALL in one (1).pdf
- 1. Machine Learning 1 Machine Learning 💡 K Nearest Neighbour Classification Here answer is Pass
- 5. Machine Learning 5 💡 Weighted KNN Final answer is False
- 7. Machine Learning 7 💡 K means Clustering
- 9. Machine Learning 9 💡 Principal Component Analysis It Reduces overfitting problem It reduces dimension (Features) to solve problem of overfitting Number of Principle component can be less than or equal to Number of features PC1 will have highest Priority All Principle components should be independent Example➖ Steps Find mean Find covarainace matrix
- 10. Machine Learning 10 Steps➖ Find Eigen Values Lambda1 and Lambda2 For each eigen value , find eigen vector This 2 eigen vectors are our final answer
- 11. Machine Learning 11 PRACTICE PROBLEMS BASED ON PRINCIPAL COMPONENT ANALYSIS- Consider the two dimensional patterns (2, 1), (3, 5), (4, 3), (5, 6), (6, 7), (7, 8). Compute the principal component using PCA Algorithm. Solution- We use the above discussed PCA Algorithm- Step-01: Get data. The given feature vectors are- x = (2, 1) 1 x = (3, 5) 2 x = (4, 3) 3
- 12. Machine Learning 12 x = (5, 6) 4 x = (6, 7) 5 x = (7, 8) 6 Step-02: Calculate the mean vector (µ). Mean vector (µ) = ((2 + 3 + 4 + 5 + 6 + 7) / 6, (1 + 5 + 3 + 6 + 7 + 8) / 6) = (4.5, 5) Thus, Step-03: Subtract mean vector (µ) from the given feature vectors. x – µ = (2 – 4.5, 1 – 5) = (-2.5, -4) 1 x – µ = (3 – 4.5, 5 – 5) = (-1.5, 0) 2 x – µ = (4 – 4.5, 3 – 5) = (-0.5, -2) 3 x – µ = (5 – 4.5, 6 – 5) = (0.5, 1) 4 x – µ = (6 – 4.5, 7 – 5) = (1.5, 2) 5 x – µ = (7 – 4.5, 8 – 5) = (2.5, 3) 6 Feature vectors (xi) after subtracting mean vector (µ) are- Step-04: Calculate the covariance matrix. Covariance matrix is given by-
- 13. Machine Learning 13 Now, Now, Covariance matrix = (m1 + m2 + m3 + m4 + m5 + m6) / 6 On adding the above matrices and dividing by 6, we get- Step-05: Calculate the eigen values and eigen vectors of the covariance matrix. λ is an eigen value for a matrix M if it is a solution of the characteristic equation |M – λI| = 0. So, we have- From here, (2.92 – λ)(5.67 – λ) – (3.67 x 3.67) = 0 16.56 – 2.92λ – 5.67λ + λ2 – 13.47 = 0 λ2 – 8.59λ + 3.09 = 0 Solving this quadratic equation, we get λ = 8.22, 0.38 Thus, two eigen values are λ1 = 8.22 and λ2 = 0.38. Clearly, the second eigen value is very small compared to the first eigen value.
- 14. Machine Learning 14 So, the second eigen vector can be left out. Eigen vector corresponding to the greatest eigen value is the principal component for the given data set. So. we find the eigen vector corresponding to eigen value λ1. We use the following equation to find the eigen vector- MX = λX where- M = Covariance Matrix X = Eigen vector λ = Eigen value Substituting the values in the above equation, we get- Solving these, we get- 2.92X1 + 3.67X2 = 8.22X1 3.67X1 + 5.67X2 = 8.22X2 On simplification, we get- 5.3X1 = 3.67X2 ………(1) 3.67X1 = 2.55X2 ………(2) From (1) and (2), X1 = 0.69X2 From (2), the eigen vector is- Thus, principal component for the given data set is- Lastly, we project the data points onto the new subspace as-
- 15. Machine Learning 15 💡 Conditional Probability & Bayes Theorem
- 16. Machine Learning 16 💡 Naive Bayes Classifier Answer is banana
- 20. Machine Learning 20 B is the winner
- 25. Machine Learning 25 💡 Logistic Regression
- 30. ENDS
- 31. ENTROPY OF TRUE EXAMPLE
- 37. ID3 Decision tree Learning Algorithm
- 38. • EXAMPLE-1 WHOLE ENTROPY ATTRIBUTE WITH MAX INFO GAIN IS ROOT NODE
- 44. • EAXMPLE – 2
- 46. ENSEMBLE TECHNIQUE In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
- 49. t – target output o – obtained output
- 53. Hidden layer
- 54. EXAMPLE - 1
- 57. CNN • https://www.youtube.com/watch?v=cleLMnmNMpY • https://www.youtube.com/watch?v=Etksi- F5ug8&t=1s • https://www.youtube.com/watch?v=PGBop7Ka9AU& t=9s • https://www.youtube.com/watch?v=aLjpU-ahEhA • https://www.youtube.com/watch?v=VpSLtKiPhLM • Solved example:- https://www.youtube.com/watch?v=AN9sZUFyxCo