CS-UY 4563 - Introduction to Machine Learning

Final ProjectCS-UY 4563 - Introduction to Machine Learning

Overview

• Partner with one student and select a machine learning problem of your choice.
• Apply the machine learning techniques you’ve learned during the course toyour chosen problem.

• Present your project to the class at the semester’s end.

Submission Requirements on Gradescope Submit the following on Gradescope by the evening before the first presentation (exactdate to be announced):

• Presentation slides.
• Project write-up (PDF format).
• Project code as a Jupyter Notebook. If necessary, a GitHub link is acceptable.
• If using a custom dataset, upload it to Gradescope (or provide a GitHub link, ifnecessary).1Project Guidelines

Write-Up Requirements Your project write-up should include the following:

1. Introduction: Describe your data set and the problem you aim to solve.
2. Perform some unsupervised analysis:

• Explore pattern or structure in the data using clustering and dimensionality (e.gPCA).

• Visualize the training data1 :– Plot individual features to understand their distribution (e.g., histogramsor density plots).– Plot individual features and their relationship with the target variable.– Create a correlation matrix to analyze relationships between features.

• Discuss any interesting structure is present in the data. If you don’t find anyinteresting structure, describe what you tried.

3. Supervised analysis: Train at least three distinct learning models2 discussed inthe class (such as Linear Regression, Logistic Regression, SVM, Neural NetworksCNN).3For implementation, you may:

• Use your own implementation from homework or developed independently.
• Use libraries such as Keras, scikit-learn, or TensorFlow.

For each model,4 you must:

• Try different feature transformations. You should have at least three transfor

mations. For example, try the polynomial, PCA, or radial-basis function kernel.

For neural networks, different architectures (e.g., neural networks with varying

numbers of layers) can also be considered forms of feature transformations, as

they learn complex representations of the input data.

• Use different regularization techniques. You should have at least 6 differentregularization values per model1Do not look at the validation or test data.2You can turn a regression task into a classification task by binning, or for the same dataset,select adifferent feature as the target for your model. Or you can use SVR.3Ifyou wish to use a model not discussed in class, you must discuss it with me first, or you will notreceive any points for that model.4Even if you get a very high accuracy,perform these transformations to see what happens.2 Table of Results:

• Provide a table with training accuracy and validation metrics for every model.

Include results for the different parameter settings (e.g., different regularization

values).

– For classification include metrics such as precision/recall.

– For regression modes, report metrics like MSE, R2 . For example, supposeyou’re using Ridge Regression and manipulating the value of λ. In thatcase, your table shouldcontain the training and validation accuracy forevery lambda value you used.

• Plot and analyze how performance metrics (like accuracy, precision, recall, MSE)change with different feature transformations, hyperparameters (e.g.regularizationsettings, learning rate).

Analytical Discussion:

• Analyze the experimental results and explain key findings. Provide a chart ofyour key findings.

• Highlight the impact of feature transformations, regularization, and other hyperparameters on the model’s performance. Refer to the graphs provide in earliersections to support your analysis. 代写CS-UY 4563 - Introduction to Machine Learning  Focus on interpreting:– Whether the models overfit or underfit the data.– How bias and variance affect performance, and which parameter choiceshelped achieve better generalization.

Presentation Guidelines

• You and your partner will give a six-minute presentation to the class.
• Presentations will be held during the last 2 or 3 class periods and during the finalexam period for this class. You will be assigned a day for your presentation. If werun out of time the day you are to present your project, you will present the nextday reserved for presentations.

• Attendance during all presentations is required. A part of your project gradewill be based on your attendance for everyone else’s presentation.

Important Notes on Academic Integrity

• Your submission will undergo plagiarism checks.
• If we suspect you of cheating, you will receive 0 for your final project grade. See thesyllabus for additional penalties that may be applied.

3Dataset Resources Below are some resources where you can search for datasets. As a rough guideline, yourdataset should have at least 200 training examples and at least 10 features. Youare free to use these resources, look elsewhere, or create your own dataset.

Modifications

• If you have a project idea that doesn’t satisfy all the requirements mentioned above,please inform me, and we can discuss its viability as your final project.

• If you use techniques not covered in class, you must demonstrate your understandingof these ideas.Brightspace Submissions Guidelines

• Dataset and Partner: Submit the link to your chosen dataset and your partner’sname by October 30th.

• Final Submissions: Upload your presentation slides, project write-up, and code toGradescope by the evening before the first scheduled presentation. The exact datePotential Challenges and Resources As you work with your dataset, you may encounter specific challenges that require aditional techniques or tools. Below are some topics and resources that might be useful.lease explore these topics further through online research.

4• Feature Reduction: Consider using PCA (which will be covered in class). PCA isespecially useful when working with SVMs, as they can be slow with highdimensionaldata.If you choose to use SelectKBest from scikit-learn, you must understand why it worksbefore you use it.

• Creating Synthetic Examples: When using SMOTE or other methods to generatesynthetic data, ensure that only real data is used in the validation and test sets.- If using synthetic data, make sure your validation set and test set mirrors the trueclasproportions from the original dataset. A balanced test set for naturally unbalanced data can give misleading impressions of your model’s real-world performanceFor more details, see: Handling Imbalanced Classes

• Working with Time Series Data: For insights on working with time series data,visit: NIST Handbook on Time SeriesHandling Missing Feature Values:– See Lecture 16 at Stanford STATS 306B– Techniques to Handle Missing Data Values– How to Handle Missing Data in Python– Statistical Imputation for Missing Data

• Multiclass Classification:– Understanding Softmax in Multiclass Classificatio– Precision and Recall for Multiclass Metrics

• Optimizers for Neural Networks: You may use Adam or other optimizers fortraining neural networks.

• Centering Image Data with Bounding Boxes: If you are working with imagedata, you are allowed to use bounding boxes to center the objects in your images. Youcan use libraries like OpenCV (‘cv2’). Don’t forget to scale your data as part of preprocessing. Be sure to document any modifi-cations you made, including the scaling or normalization techniques you applied.The following resource might be helpful. Please stick to topics we discussed in class or==those mentioned above: CS229: Practical Machine Learning Advic5