ENG335 Machine Learning SUSS Assignment Sample Singapore

ENG335 Machine Learning course is a great opportunity for students to gain further knowledge in the field of Machine Learning. Deep learning, neural networks, and supervised and unsupervised learning are some major topics that will be addressed.The course also gives students a chance to become familiar with core data mining algorithms and techniques for fabricating results from those large data sets. It provides students with the ability to identify valuable insights from complex data sets, as well as an understanding of how these techniques can be used in real-world applications.

Moreover, it takes a comprehensive look at the essential aspects of artificial intelligence (AI) systems and helps prepare students for jobs that require expertise in this innovative technology. Ultimately, ENG335 offers an exciting journey into the world of digital innovation and intelligence gathering, giving students the skills needed to succeed in positions where machine learning meets practical modern problems.

Unlock your potential in ENG335 Machine Learning by studying the exemplary assignments of past course participants!

Singaporeassignmenthelp.com provides online assignment help to its customers. This includes detailed ENG335 Machine Learning Assignment Sample Singapore that can help students to understand the basic concepts and techniques of machine learning and apply them in their assignments. These samples are designed with step-by-step guidance, which makes it easy to learn the basics as well as advanced principles of machine learning.

Here, we will break down and explain various assignment briefs. They include:

Assignment Brief 1: Prepare data for machine learning algorithms.

Preparing data for machine learning algorithms is a crucial step in the overall machine learning process. Here are the general steps involved in data preparation:

1. Data Collection: The first step in data preparation is to collect relevant data. The data should be relevant to the problem that you want to solve with your machine learning algorithm.
2. Data Cleaning: After collecting data, the next step is to clean the data. Cleaning data involves removing any irrelevant data, handling missing data, and removing duplicate records.
3. Data Transformation: Once the data has been cleaned, the next step is to transform the data. This involves converting categorical data into numerical data, normalizing data, and scaling data.
4. Feature Selection: Feature selection involves selecting the most important features in the data that are relevant to the problem being solved. This can be done by using statistical methods or machine learning algorithms.
5. Splitting Data: The next step is to split the data into training and testing sets. The training set is used to train the machine learning algorithm, while the testing set is used to evaluate the performance of the model.
6. Data Augmentation: Data augmentation is a technique that involves creating new data from the existing data. This can be done by applying various transformations to the data, such as rotation, flipping, and cropping.
7. Feature Engineering: Feature engineering involves creating new features from the existing data. This can be done by applying mathematical operations to the data or by using domain-specific knowledge to create new features.
8. Data Balancing: Data balancing involves ensuring that the data is balanced. This means that there are an equal number of records for each class in the data. This is important because an imbalanced dataset can lead to biased results.
9. Preprocessing: The final step in data preparation is preprocessing. This involves applying any final preprocessing steps to the data before feeding it into the machine learning algorithm.

Assignment brief 2: Construct support vector machines for classification.

Support vector machines (SVMs) are a popular machine learning algorithm used for classification problems. The goal of SVMs is to find the optimal hyperplane that separates the data into different classes.

Here are the general steps for constructing an SVM for classification:

1. Data Preprocessing: Prepare your data by cleaning, normalizing, and transforming it into the format that can be fed into an SVM algorithm.
2. Selecting Features: Identify which features are most important for your classification problem. This process is called feature selection or feature engineering.
3. Splitting Data: Split the dataset into training and testing sets. The training set is used to train the SVM model, and the testing set is used to evaluate the model’s performance.
4. Choosing a Kernel: SVMs use a kernel function to transform the data into a higher-dimensional space where the classes are more easily separable. Common kernel functions include linear, polynomial, and radial basis function (RBF).
5. Training the Model: Use the training set to train the SVM model. During training, the SVM will identify the optimal hyperplane that maximizes the margin between the two classes. The margin is the distance between the hyperplane and the closest data points from each class.
6. Tuning Hyperparameters: SVMs have several hyperparameters that can be tuned to improve their performance. Examples include the penalty parameter C, which controls the balance between maximizing the margin and minimizing the classification error, and the gamma parameter, which controls the shape of the kernel function.
7. Testing the Model: Once the model is trained and the hyperparameters are tuned, use the testing set to evaluate the model’s performance. Calculate metrics such as accuracy, precision, and recall to assess how well the model is performing.
8. Deploying the Model: If the model performs well on the testing set, it can be deployed to make predictions on new, unseen data.

Assignment Brief 3: Set up decision trees, random forest for classification.

Decision Trees

A decision tree is a tree-like model where each node represents a feature, each branch represents a decision rule, and each leaf represents a classification or decision. Here are the steps to set up a decision tree for classification:

1. Collect data: Gather a dataset that includes features and their corresponding labels or classes.
2. Split data: Divide the dataset into two subsets: a training set and a testing set.
3. Train the model: Build a decision tree using the training set. In the decision tree, each node is a feature, and each branch represents a decision rule. The goal is to create a tree that accurately classifies the data.
4. Test the model: Use the testing set to evaluate the accuracy of the decision tree. Calculate the accuracy of the model by comparing the predicted labels to the actual labels.
5. Improve the model: If the accuracy of the model is not satisfactory, adjust the decision tree by modifying the decision rules, adding or removing features, or changing other parameters. Then repeat steps 3 and 4.

Random Forests

A random forest is an ensemble model that combines multiple decision trees to improve classification accuracy. Here are the steps to set up a random forest for classification:

1. Collect data: Gather a dataset that includes features and their corresponding labels or classes.
2. Split data: Divide the dataset into two subsets: a training set and a testing set.
3. Train the model: Build multiple decision trees using a random subset of the training set. The number of decision trees is determined by a parameter called the number of estimators. Each decision tree is built using a different subset of the features and training data. The goal is to create multiple trees that accurately classify the data.
4. Test the model: Use the testing set to evaluate the accuracy of the random forest. Calculate the accuracy of the model by comparing the predicted labels to the actual labels.
5. Improve the model: If the accuracy of the model is not satisfactory, adjust the random forest by modifying the decision trees, adding or removing features, or changing other parameters. Then repeat steps 3 and 4.

Assignment Brief 4: Rate the performance of clustering algorithm.

1. The quality and representativeness of the data: The clustering algorithm’s performance depends on the quality and representativeness of the data being used. If the data is noisy, sparse, or contains outliers, the clustering algorithm’s performance may be affected.
2. The choice of clustering algorithm: Different clustering algorithms have different strengths and weaknesses. Some algorithms work well with spherical clusters, while others are better suited for non-spherical clusters. The choice of clustering algorithm depends on the nature of the data and the clustering problem at hand.
3. The choice of distance or similarity metric: Clustering algorithms depend on a distance or similarity metric to measure the distance between data points. The choice of metric can affect the clustering algorithm’s performance, as some metrics work better with certain types of data.
4. The number of clusters: The number of clusters needs to be chosen carefully. If the number of clusters is too high, the clustering algorithm may overfit the data. If the number of clusters is too low, the clustering algorithm may miss important structure in the data.
5. The evaluation metrics used: Different evaluation metrics can be used to evaluate the performance of a clustering algorithm. Some common metrics include silhouette score, purity, and completeness. The choice of metric depends on the specific clustering problem at hand.

Assignment Brief 6: Design neural network based classifiers.

Designing a neural network-based classifier involves several steps, including choosing an appropriate architecture, defining the input and output layers, selecting the activation functions, and specifying the training algorithm. Here’s a general process that can be followed:

1. Define the problem: Determine what problem the classifier needs to solve, such as binary classification or multi-class classification.
2. Collect and preprocess the data: Gather data and preprocess it by scaling, normalizing, or encoding categorical variables as necessary.
3. Choose an appropriate neural network architecture: Decide on an appropriate neural network architecture based on the problem, such as a feedforward neural network, convolutional neural network, or recurrent neural network.
4. Define the input and output layers: Specify the number of input and output nodes based on the data and problem.
5. Select the activation functions: Select appropriate activation functions for each layer, such as sigmoid, ReLU, or tanh.
6. Define the loss function: Choose a suitable loss function based on the problem, such as cross-entropy for classification tasks.
7. Specify the training algorithm: Choose a training algorithm, such as stochastic gradient descent or Adam, and specify the learning rate, batch size, and number of epochs.
8. Train and evaluate the model: Train the model on the training data and evaluate its performance on the validation set. Adjust the hyperparameters as necessary to improve the accuracy.
9. Test the model: Finally, test the model on a separate test set to evaluate its performance on new, unseen data.

Assignment Brief 7: Propose suitable machine learning algorithms.

There are many machine learning algorithms available, and the choice of algorithm depends on the specific problem you are trying to solve, the size and complexity of your dataset, and your computational resources. Here are some commonly used machine learning algorithms for different types of problems:

1. Linear Regression: This algorithm is used for predicting a continuous output variable based on one or more input variables. It works by fitting a straight line to the data that minimizes the difference between the predicted values and the actual values.
2. Logistic Regression: This algorithm is used for classification problems where the output variable is binary (e.g., yes/no, true/false). It works by fitting a sigmoid curve to the data that separates the two classes.
3. Decision Trees: This algorithm is used for both regression and classification problems. It works by splitting the data into smaller subsets based on the most important features, and then recursively splitting those subsets until a stopping criterion is met.
4. Random Forest: This algorithm is an extension of decision trees and is used for classification and regression problems. It works by creating multiple decision trees and then aggregating their predictions to improve the overall accuracy.
5. Support Vector Machines (SVM): This algorithm is used for classification and regression problems. It works by finding the hyperplane that separates the data into the different classes or predicts the output variable, while maximizing the margin between the classes.
6. K-Nearest Neighbors (KNN): This algorithm is used for classification and regression problems. It works by finding the k closest data points in the training set to the new data point, and then making a prediction based on the majority class or the average of the k nearest neighbors.
7. Naive Bayes: This algorithm is used for classification problems. It works by calculating the probability of each class given the input features, and then choosing the class with the highest probability.
8. Neural Networks: This algorithm is used for both classification and regression problems. It works by creating a network of interconnected nodes that can learn to recognize patterns in the data through a process of trial and error.

Assignment Brief 8: Estimate the performance metrics of learning algorithms.

Performance metrics are used to evaluate the effectiveness of machine learning algorithms. The choice of metrics depends on the specific problem and the type of learning algorithm used. Some common performance metrics used for classification and regression problems are:

For classification problems:

• Accuracy: the proportion of correct predictions made by the algorithm.
• Precision: the proportion of true positives (correctly identified instances) among the predicted positives.
• Recall: the proportion of true positives among the actual positives.
• F1 score: a weighted average of precision and recall.
• Area Under the ROC Curve (AUC-ROC): measures the trade-off between true positive rate and false positive rate.

For regression problems:

• Mean Squared Error (MSE): measures the average squared difference between the predicted and actual values.
• Root Mean Squared Error (RMSE): the square root of the mean squared error.
• Mean Absolute Error (MAE): measures the average absolute difference between the predicted and actual values.
• R-squared: measures how well the model fits the data relative to a baseline model.

To estimate the performance metrics of a learning algorithm, you can use a holdout set or cross-validation. In a holdout set approach, you split the data into training and testing sets, train the model on the training set, and evaluate its performance on the testing set. In cross-validation, you divide the data into k-folds, train the model on k-1 folds, and test it on the remaining fold. You repeat this process k times, with each fold serving as the testing set once. This allows you to get a more reliable estimate of the model’s performance.

Assignment Brief 9: Assess the impact of hardware performance on the machine learning algorithms.

Hardware performance can have a significant impact on the performance of machine learning algorithms, especially for computationally intensive tasks like training deep neural networks. Here are some ways in which hardware performance can affect machine learning:

1. Processing speed: Machine learning algorithms can require a lot of computation, and faster processors can significantly speed up the execution of these algorithms. A faster processor can mean quicker training times, allowing data scientists to experiment with more models or try more complex models.
2. Memory: Machine learning algorithms often require large amounts of memory, especially for large datasets or complex models. Having enough RAM can allow data scientists to train larger models without running into memory constraints.
3. Graphics Processing Units (GPUs): GPUs are specifically designed for parallel processing and are ideal for accelerating machine learning algorithms. Many deep learning frameworks, such as TensorFlow and PyTorch, have GPU support that allows models to be trained faster than with traditional CPUs.
4. Storage: Large datasets are often required for training machine learning models. Fast storage, such as solid-state drives (SSDs), can help with data loading times, which can be a bottleneck in machine learning pipelines.
5. Distributed computing: Distributed computing, using multiple computers to work together on a task, can speed up machine learning algorithms even further. However, this requires additional hardware and software infrastructure to set up and maintain.