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Python代写 | EE658 Machine Learning in Engineering Assignment

本次美国CS代写主要是Python机器学习相关

Spring 2021

EE658 Machine Learning in Engineering Assignment #3: Dimensionality reduction Due Date: Tuesday, April 27th, 2021

This assignment is divided into three parts described below. Please submit your work as a Jupyter Notebook file along with a short video (5 to 7 minutes) where you demonstrate the execution of your code and the results obtained. The video should be narrated with your own commentary.

Part I:

  1. Load the Digits dataset from the Scikit-learn library. The data consists of 1797 images of hand-written digits, where the resolution of the images is 8×8.
  2. Split the dataset into training and testing.
  3. Train a neural network using the training data.
  1. Use the neural network to make predictions on the testing data.
  2. Compute the accuracy score and the confusion matrix for the testing data

Part II:

  1. Use the Principal Components Analysis (PCA) method to find the Eigenvalues and Eigenvectors associated with the Digits dataset.
  2. Choose an appropriate number of principal components to transform the data. Justify your selection by showing the Scree plot.
  3. Repeat questions 2 through 5 of Part I
  4. Compare the results of Part I with Part II

Part III:

  1. Use the Singular Value Decomposition (SVD) to decompose 𝑋 and verify that: 𝑋 = 𝑈 Σ 𝑉𝑇
  2. Transform the original dataset by using the eigenvectors matrix.
  3. Choose an appropriate number of principal components to transform the data. Justify yourselection by showing the Scree plot.
  4. Repeat questions 2 through 5 of Part I
  5. Compare results of Part II and Part III

程序辅导定制C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


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