K. N. Toosi University of Technology
Instructor: Dr. Babak Naser Sharif
Course Level: Master’s & PhD
Teaching Assistant: Mehran Tamjidi
This repository contains all homework assignments and supplementary resources for the Machine Learning course offered in Fall 1403 (2024) at K. N. Toosi University of Technology. The course covers the mathematical foundations, core algorithms, and deep learning techniques essential to the field of machine learning, with practical implementations using Python and PyTorch.
- 📐 Linear Algebra & Probability Review
- 🔢 Linear Regressors
- 📈 General Regression Techniques
- 🧮 Linear Classifiers
- 🧠 Fisher’s Linear Discriminant
- ➕ Logistic Regression
- 💡 Support Vector Machines (SVM)
- 🔁 Support Vector Regression (SVR)
- 🧱 Shallow Neural Networks
- 🧲 Optimization Algorithms
- 🏗️ Deep Neural Networks
- ⏳ Recurrent Networks (LSTM & GRU)
- 🧠 Convolutional Neural Networks (CNN)
- 🌐 Transformer Architectures
- 🌌 Variational Autoencoders (VAE)
- 🌀 Generative Adversarial Networks (GAN)
- 👣 k-Nearest Neighbors (k-NN)
- 🌳 Decision Trees
- Python 3.10+
- PyTorch
- NumPy, SciPy, Matplotlib
- Jupyter Notebooks
By the end of this course, students will be able to:
- Understand the theoretical and mathematical foundations of machine learning.
- Implement a variety of supervised and unsupervised learning models.
- Apply optimization techniques for training deep networks.
- Explore state-of-the-art architectures like CNNs, RNNs, and Transformers.
- Build generative models using VAEs and GANs.
All assignments must be completed individually unless otherwise stated. Discussions for conceptual clarity are encouraged, but direct sharing of code or answers is strictly prohibited.
If this course material is helpful in your research or academic work (some content is in Persian), please cite as:
Machine Learning, Dr. Babak Naser Sharif, K. N. Toosi University of Technology, Fall 1403 (2024).