My Machine Learning course assignments.
- Lab 1: Learn basic operations with NumPy.
- Lab 2: Learn basic operations with Matplotlib.
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Lab 3: Perform linear regression to fit a curve for given
xandyscatter points. - Lab 4: Use the data from Lab 3 to fit the curve using the gradient descent method.
- Lab 4-Extra: Plot the loss trajectory for the gradient descent method in Lab 4.
- Lab 5: Implement a logistic regression model using gradient descent on a given dataset and visualize the training samples along with the decision boundary in 2D.
- Lab 6: Use Linear Discriminant Analysis (LDA) to classify the dataset from Lab 5, calculate the sigmoid function for estimation, and plot the decision boundary in 2D.
- Lab 7: Use Decision Tree to classify the dataset from Lab 5. Instead of a decision boundary, visualize the classification area in 2D.
- Lab 8: Train an SVM model using sklearn's SVC with a linear kernel on 15% noisy training dataset from Lab 5, and visualize both the training samples and classification area in 2D.
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Lab 9: Transform FashionMNIST images to 784D vectors, apply PCA for dimensionality reduction to
$d$ , and use K-NN classification on the reduced test data. - Final Lab: Train a Random Forest model with up to 10 binary trees (max depth 5) using only NumPy and pandas for MNIST dataset classification.
pip install -r requirements.txt
cd lab_directory # Replace 'lab_directory' with the specific lab folder name
python main.py