A repository for solutions of ML assignment of SUT ML course taught by Prof. Shamsollahi
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Assignment 1
- Part 1: Introduction to pytorch and tensors and a code for SVD for denoising images
- Part 2: PCA for dimensionality reduction
- Part 3: Autograd, learning process and PyTorch
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Assignment 2
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Part 1: Implementing the following classifiers for the breast canser dataset:
- perceptron
- KNN
- SVM
- Bayesian
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Part 2: Implementing the Parzen window for estimating a distribution
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Assignment 3
- Part 1: Feature extraction and dimensionality reduction techniques like autoencoders and kernel PCA.
- Part 2: K-means Clustering algorithm
- Part 3: Implementing the Expectation-Maximization (EM) algorithm from scratch to train a Gaussian Mixture Model (GMM) on image datasets.
- Part 4: Training a Random Forest model. An ensemble method is implemented that combines predictions from multiple models with max voting
- Part 5: build a basic recommender system using the Spotify dataset. This section includes feature selection process.
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Assignment 4
- Part 1: Fine tunning a model
- Part 2: A code for using decision tree for classification