🤖 Machine Learning / Deep Learning

This repository contains the assignments and their solutions for the Machine/Deep Learning course at TeIAS, with implementations using PyTorch and Scikit-learn.

Assignments

Assignment 1: Theoretical Foundations of Machine Learning

• Linear regression with closed-form solution for both one- and multi-dimensional outputs.
• Maximum Likelihood Estimation (MLE) and Maximum a Posteriori (MAP) estimation.
• Bias-Variance trade-off

Assignment 2: Regression and Model Optimization

• Logistic Regression, Linear Regression, Gradient Descent, and Regularization techniques.

Assignment 3: Neural Networks and Training Challenges

• Neural Network implementation, training, and addressing the vanishing gradient problem.

Assignment 4: RNNs and LSTMs

• Implementation and training of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks on various datasets.

Assignment 5: Advanced Applications in Deep Learning

• Image segmentation using the Encoder-Decoder framework, and fine-tuning BERT for sentiment analysis using the Hugging Face Trainer.

Contributing

Feel free to submit issues or pull requests if you find bugs or want to improve this repository.

Acknowledgements

This work was completed as part of the Machine Learning / Deep Learning course taught at Tehran Institute for Advanced Studies (TeIAS), Khatam University.

License

This project is open-source and available under the MIT License.