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Mastering Machine Learning: A Comprehensive Guide for Students

Introduction to Machine Learning

Machine learning, a pivotal branch of artificial intelligence (AI), empowers computers to replicate human learning processes, enhancing their abilities automatically through algorithms and statistical models. This field is integral for students aiming to excel in the domains of advanced data science, mathematics, and computer science. Mastering machine learning requires a deep understanding of complex subjects, setting the foundation for innovative solutions in technology and beyond.

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Struggling with machine learning assignments? Our homework help service offers expert assistance, providing clear code examples, detailed explanations, and documentation to support your learning. Additionally, our Homework Library serves as a quick reference, offering solutions to common machine learning challenges.

Key Machine Learning Topics Covered

Our tutoring service encompasses all aspects of machine learning, including but not limited to:

Artificial Intelligence: Dive into the world of AI, where computers emulate human intelligence across tasks like decision-making and speech recognition.
Computer Science: Explore the foundations of computing, including software and hardware intricacies.
Algorithms: Understand the essence of algorithms and their role in solving computational problems.
Python Programming: Gain proficiency in Python, a premier language for machine learning development.
Supervised and Unsupervised Learning: Master these core machine learning approaches, from working with labeled datasets to identifying patterns in untagged data.

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Recently Asked Machine Learning Questions

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Q1:Q1 Consider the problem where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age.See Answer
Q2:For this programming assignment you will implement the Naive Bayes algorithm from scratch and the functions to evaluate it with a k-fold cross validation (also from scratch). You can use the code in the following tutorial to get started and get ideas for your implementation of the Naive Bayes algorithm but please, enhance it as much as you can (there are many things you can do to enhance it such as those mentioned at the end of the tutorial):See Answer
Q3:Q1 Consider the problem where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age. a) Using Cartesian distance, Manhattan distance and Minkowski distance of order 3 as the similarity measurements show the results of the gender prediction for the Evaluation data that is listed below generated training data for values of K of 1, 3, and 7. Include the intermediate steps (i.e., distance calculation, neighbor selection, and prediction). b) Implement the KNN algorithm for this problem. Your implementation should work with different training data sets as well as different values of K and allow to input a data point for the prediction. c) To evaluate the performance of the KNN algorithm (using Euclidean distance metric), implement a leave- one-out evaluation routine for your algorithm. In leave-one-out validation, we repeatedly evaluate the algorithm by removing one data point from the training set, training the algorithm on the remaining data set and then testing it on the point we removed to see if the label matches or not. Repeating this for each of the data points gives us an estimate as to the percentage of erroneous predictions the algorithm makes and thus a measure of the accuracy of the algorithm for the given data. Apply your leave-one-out validation with your KNN algorithm to the dataset for Question 1 c) for values for K of 1, 3, 5, 7, 9, and 11 and report the results. For which value of K do you get the best performance? d) Repeat the prediction and validation you performed in Question 1 c) using KNN when the age data is removed (i.e. when only the height and weight features are used as part of the distance calculation in the KNN algorithm). Report the results and compare the performance without the age attribute with the ones from Question 1 c). Discuss the results. What do the results tell you about the data?See Answer
Q4:Q2. Using the data from Problem 2, build a Gaussian Naive Bayes classifier for this problem. For this you have to learn Gaussian distribution parameters for each input data feature, i.e. for p(height|W), p(height|M), p(weight|W), p(weight|M), p(age|W), p(age|M). a) Learn/derive the parameters for the Gaussian Na ive Bayes Classifier for the data from Question 2 a) and apply them to the same target as in problem 1a). b) Implement the Gaussian Na ive Bayes Classifier for this problem. c) Repeat the experiment in part 1 c) and 1 d) with the Gaussian Native Bayes Classifier. Discuss the results, in particular with respect to the performance difference between using all features and using only height and weight. d) Same as 1d but with Naïve Bayes. e) Compare the results of the two classifiers (i.e., the results form 1 c) and 1d) with the ones from 2 c) 2d) and discuss reasons why one might perform better than the other.See Answer
Q5:Question 1 Download the SGEMM GPU kernel performance dataset from the below link. https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance Understand the dataset by performing exploratory analysis. Prepare the target parameter by taking the average of the THREE (3) runs with long performance times. Design a linear regression model to estimate the target using only THREE (3) attributes from the dataset. Discuss your results, relevant performance metrics and the impact of normalizing the dataset.See Answer
Q6:This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. The code as it is, the model accuracy is 79 The goal is to modify the code to be able to get an accuracy of the model in the high 80s and low 90s. In box 3 of the code, there are the hyperparameters that need to be tuned and the pipeline that might need to be modifed. Voting model can be used to get high accuracy. We need to improve the model accuracy from the existing code. Info about the dataset: The dataset is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are right-hand. Each participant wrote each character (from 'alef' to 'yeh') ten times on two forms. The forms were scanned at the resolution of 300 dpi. The dataset is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set were not from a single institution (to ensure variability of the test set). The code: This is a machine learning model in python using scikit learn to classify the handwritten Arabic letters. There are two files. The train data and the test data. The code is available, and we need to optimize the code so under box number 6 when we do the cross validation of the model, the accuracy of the model should be in high 80s and low 90s. we should be tuning the hyperparameters and improve the pipeline as needed. Anything is allowed to be used from the scikit learn but nothing more. Voting model can be used to improve accuracy. Goal: build an image classifier to classify handwritten Arabic language characters using scikit learn. The model accuracy have to be in high 80s like 89% or low 90s like 92% This is all about tuning the hyperparameters and the model pipelineSee Answer
Q7:There are four folders, each folder contains a set of exercises, the expected results are written at the top of each ipynb. some files are just example solutions Day 1 all about fitting a linear regression or logistic regression to the data. Also to determine the decision boundaries. Day 2 Use Neural Networks to solve simple classification examples Day 3 Using Convolutional Neural Network with PyTorch with one example solution Day 4 Deep learning, the solution is ready just we add the testing data and test the built model and output a submission file with labelsSee Answer
Q8:The main aim of this project is to analyze a movie review's textual content in order to determine its underlying sentiment. In this project, we try to classify whether a person liked the movie or not based on the review they give for the movie. 1) You need to develop a python code to calculate the sentiment using NLP analysis and should use CNN and logisitic regression 2) You need to create a report of what you have done in the code and also you need to explain how our work is different from the references we have taken (references are in the document)See Answer
Q9:CSE 6363 - Machine Learning Data Set Use the dataset given at the bottom of this file. Do Not Use You are not allowed to use any ML libraries other than NumPy. You cannot use sklearn or any ML library. If used, you will receive a penalty of 90 points. You cannot use pandas. If used, you will receive a penalty of 20 points. Libraries You are allowed to use NumPy, math. You can use matplotlib to plot graphs. If you want to use any other library apart from these, please check with your GTA and get their approval. Where to code 1. We will provide you with a directory structure with python files for each part of every question. You must write your code in these files. 2. It will contain a script to execute the files. You must run this script and verify that your code runs before you submit. To run this script you must make it executable first or else you will get permission denied error.See Answer
Q10:2. Problem 2: In this problem, we utilize Tensorflow and keras to train a 2-hidden layers regression (M = 1) NN using the Boston Housing price regression dataset ¹. In this dataset, we have N = 404 data points where each one consists of F = 13 features. The output of the network (scalar) isSee Answer
Q11:In this problem, similar to Problem 2, we utilize Tensorflow and keras to train a 1- hidden layer classification NN using the MNIST digits (M = 10) dataset 2. In this dataset, we have N = 60000 training data points and 10000 examples for testing where each one consists of a gray scale image with F= 28 * 28 = 784 features (or pixels). See Answer
Q12:4. Problem 4: In this problem, we utilize Tensorflow and keras to train a convolutional NN for clas- sification using the Fashion MNIST (M = 10) dataset ³. In this dataset, we have N = 60000 training data points and 10000 examples for testing where each one consists of a gray scale image with F = 28 * 28 = 784 features (or pixels). The dataset is loaded and pre-processed using the following code.See Answer
Q13:1. Consider the problem from the previous assignments where we want to predict gender from information about height, weight, and age. We will use Decision Trees to make this prediction. Note that as the data attributes are continuous numbers you have to use the ≥ attribute and determine a threshold for each node in the tree. As a result, you need to solve the information gain for each threshold that is halfway between two data points and thus the complexity of the computations increases with the number of data items. a) Implement a decision tree learner for this particular problem that can derive decision trees with an arbitrary, pre- determined depth (up to the maximum depth where all data sets at the leaves are pure) using the information gain criterion. b) Divide the data set from Question 1c) in Project 1 (the large training data set) into a training set comprising the first 50 data points and a test set consisting of the last 70 data elements. Use the resulting training set to derive trees of depths 1-5 and evaluate the accuracy of the resulting trees for the 50 training samples and for the test set containing the last 70 data items. Compare the classification accuracy on the test set with the one on the training set for each tree depth. For which depths does the result indicate overfitting?See Answer
Q14:1. Consider the problem from the previous assignments where we want to predict gender from information about height, weight, and age. We will use Decision Trees to make this prediction. Note that as the data attributes are continuous numbers you have to use the ≥ attribute and determine a threshold for each node in the tree. As a result, you need to solve the information gain for each threshold that is halfway between two data points and thus the complexity of the computations increases with the number of data items. a) Implement a decision tree learner for this particular problem that can derive decision trees with an arbitrary, pre- determined depth (up to the maximum depth where all data sets at the leaves are pure) using the information gain criterion. b) Divide the data set from Question 1c) in Project 1 (the large training data set) into a training set comprising the first 50 data points and a test set consisting of the last 70 data elements. Use the resulting training set to derive trees of depths 1 - 5 and evaluate the accuracy of the resulting trees for the 50 training samples and for the test set containing the last 70 data items. Compare the classification accuracy on the test set with the one on the training set for each tree depth. For which depths does the result indicate overfitting?See Answer
Q15:Decision Trees: 1. Consider the problem from the previous assignments where we want to predict gender from information about height, weight, and age. We will use Decision Trees to make this prediction. Note that as the data attributes are continuous numbers you have to use the 2 attribute and determine a threshold for each node in the tree. As a result, you need to solve the information gain for each threshold that is halfway between two data points and thus the complexity of the computations increases with the number of data items. a) Implement a decision tree learner for this particular problem that can derive decision trees with an arbitrary, pre- determined depth (up to the maximum depth where all data sets at the leaves are pure) using the information gain criterion. b) Divide the data set from Question 1c) in Project 1 (the large training data set) into a training set comprising the first 50 data points and a test set consisting of the last 70 data elements. Use the resulting training set to derive trees of depths 1-5 and evaluate the accuracy of the resulting trees for the 50 training samples and for the test set containing the last 70 data items. Compare the classification accuracy on the test set with the one on the training set for each tree depth. For which depths does the result indicate overfitting?See Answer
Q16: Introduction to Machine Learning 1- I invited six friends to watch a basketball game at home. They brought the following items along. I noticed that my friends often brought Cheese, Soda, and Wing together. Since I prefer to spend on food instead of Soda, I study how likely my friends would bring Soda if they already bought Cheese and Wing. Therefore, please calculate the Lift of this association rule {Cheese, Wing} ==> {Soda} for me. See Answer
Q17:This question walks you through the typical process of discovering association rules. We will use the market basket data in the Groceries.csv file to discover association rules. Here are the data contents. 1- Customer: Customer Identifier 2- Item: Name of Product Purchased For your information, we have sorted the observations in ascending order first by Customer and then by Item. Also, we have removed duplicated items for each customer. A- What is the number of items in the Universal Set? What is the maximum number of itemsets that we can find in theory from the data? What is the maximum number of association rules that we can generate in theory from the data? B- We are interested in the itemsets that can be found in the market baskets of at least seventy-five (75) customers. How many itemsets did we find? Also, what is the largest number of items, i.e., among these itemsets? C- We will use up to the largest value we found in Part (b) and then generate the association rules whose Confidence metrics are greater than or equal to 1%. How many association rules can we find? Next, we plot the Support metrics on the vertical axis against the Confidence metrics on the horizontal axis for these association rules. We will use the Lift metrics to indicate the size of the marker. We will add a color gradient legend to the chart for the Lift metrics. D- Among the rules that you found in Part (c), list the rules whose Confidence metrics are greater than or equal to 60%. Please show the rules in a table that shows the Antecedent, the Consequent, the Support, the Confidence, the Expected Confidence, and the Lift. See Answer
Q18:This question demonstrates the effect of rescaling input variables on the cluster results. We will discover clusters using all the observations in the TwoFeatures.csv file with the following specifications. • The input interval variables are x1 and x2 • The metric is the Manhattan distance • The minimum number of clusters is 1 • The maximum number of clusters is 8 • Use the Elbow value for choosing the optimal number of clusters Since the sklearn.cluster.KMeans class works only with the Euclidean distance, you will need to develop custom Python codes to implement the K-Means algorithm with the Manhattan distance. A- Plot x2 (vertical axis) versus x1 (horizontal axis). Add gridlines to both axes. Let the graph engine chooses the tick marks. How many clusters do you see in the graph? B- Discover the optimal number of clusters without any transformations. List the number of clusters, the Total Within-Cluster Sum of Squares (TWCSS), and the Elbow values in a table. Plot the Elbow Values versus the number of clusters. How many clusters do you find? What are the centroids of your optimal clusters? C- Linearly rescale x1 such that the resulting variable has a minimum of zero and a maximum of ten. Likewise, rescale x2. Discover the optimal number of clusters from the transformed observations. List the number of clusters, the Total Within-Cluster Sum of Squares (TWCSS), and the Elbow values in a table. Plot the Elbow Values versus the number of clusters. How many clusters do you find? What are the centroids of your optimal clusters in the original scale of x1 and x2? D- If you are doing everything correctly, you should discover two different optimal cluster solutions. In your words, how do you explain the difference? See Answer
Q19:In this problem we will compare the performance of traditional least squares, ridge regression, and the LASSO on a real-world dataset. We will use the "Boston House Prices" dataset which contains the median sale price (as of some point in the 1970's, when the dataset was created) of owner occupied homes in about 500 different neighborhoods in the Boston a rea, a long with 13 features for each home that might be relevant. These features include factors such as measures of the crime rate; measures of school quality; various measures of density; proximity to things like highways, major employment centers, the Charles River; pollution levels; etc.¹ To judge the quality of each approach, split the dataset into a training set and a testing set. The training set should consist of 400 observations, and use the remaining observations for testing. Before training any of these algorithms, it is a good idea to "standardize" the data. By this, I mean that you should take each feature (i.c., cach column of the matrix X) and subtract off its mean and divide by the standard deviation to make it zero mean and unit variance. Otherwise, the regularized methods will implicitly be placing bigger penalties on using features which just happen to be scaled to have small variance. You should determine how to "standardize" your training data by appropriately shifting/scaling cach feature using only the training data, and then apply this transformation to both the training data and the testing data so that your learned function can readily be applied to the test set. 1. First, I would like you to evaluate the performance of least squares. You should implement this yourself using the equation we derived in class. Report the performance of your algorithm in terms of mean-squared error on the test set, i.c., 2. Next, using the formula derived in class, implement your own version of ridge regression. You will need to set the free parameter A. You should do this using the training data in whatever manner you like (c.g., via a holdout set) - but you should not allow the testing dataset to influence your choice of A. Report the value of A selected and the performance of your algorithm in terms of mean-squared error on the test set. 3. Finally, I would like you to evaluate the performance of the LASSO. You do not need to implement this yourself. Instead, you can use scikit-learn's built in solver via 3 reg.fit (Xtrain, ytrain) 4 reg.predict (Xtest) 1 from sklearn import linear_model 2 reg linear_model. Lasso (alpha = ???) # Fill in alpha Above, alpha corresponds to the A parameter from the lecture notes. As in part (b), you will need to do something principled to choose a good value for this parameter. Report the value of alpha used in your code, the performance of your algorithm in terms of mean-squared error, and the number of nonzeros in 9. (You can get via reg. coef..)See Answer
Q20:In this problem I'd like you to use the following code to generate a dataset to evaluate various approaches to regression in the presence of outliers. 1 import numpy as np 2 np.random.seed (2017) 3 n = 100 4 xtrain = np.random.rand (n) 5 ytrain = 0.25 +0.5*xtrain + np. sqrt (0.1) *np.random.randn (n) 6 idx = np.random.randint (0, 100, 10) 7 ytrain [idx] = ytrain [idx] + np.random.randn (10) The code above generates training data by selecting random values for the zi's, then computing f(x) = + and adding a small amount of Gaussian noise to each observation. It then follows by creating some "outliers" in the y's by picking 10 random entries and adding a much larger amount of noise to just those elements. In the problems below, you should find a linear fit to this data. In all of the methods below, there will be one or more parameters to set. You can do this manually using whatever approach you like. (Do not go crazy optimizing these, just tune the parameters until your estimate looks reasonable.) 1. To begin, find a linear fit using the code for ridge regression that you produced in the first problem. Report the value of A that you selected, and report the slope and intercept of your linear fit. 2. Next, I would like you to find a linear fit using the Huber loss. This can be done via 1 from sklearn import linear_model 2 reg= linear_model. HuberRegressor (epsilon = 1.35, alpha=0.001) 3 reg.fit(xtrain. reshape (-1,1),ytrain) You have two parameters to choose here: € (which controls the shape of the loss function and needs to be greater than 1.0) and a (the regularization parameter). Report the values of and a you selected, and report the slope and intercept of your linear fit (see reg. intercept_ and reg.coef.).See Answer

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