Welcome to the Machine Learning Course offered by Dr. Aliyari at K. N. Toosi University of Technology! This course covers a wide range of topics in machine learning, providing you with the knowledge and skills necessary to understand and apply various machine learning algorithms.

In this course, we will dive deep into the exciting field of machine learning. You'll learn the fundamentals of machine learning, explore various algorithms, and gain practical experience through hands-on assignments and projects.

1. 📏 Prerequisites: Calculus

   • Basic concepts of calculus required for understanding machine learning algorithms.

2. 🧮 Prerequisites: Linear Algebra

   • Fundamental concepts of linear algebra essential for machine learning applications.

3. 🎲 Prerequisites: Probability

   • Understanding probability theory and statistical concepts crucial for machine learning.

4. 🐍 Prerequisites: Python

   • Basics of Python programming language necessary for implementing machine learning algorithms.

5. 🏷️ Classification Problem, Linear Classifiers, Logistic Regression

   • Introduction to classification problems, linear classifiers, and logistic regression.

6. 📊 Bayes

   • Understanding Bayesian methods in machine learning.

7. 🔽 Dimensionality Reduction - PCA

   • Principal Component Analysis (PCA) for dimensionality reduction.

8. 🔽 Dimensionality Reduction - LDA

   • Linear Discriminant Analysis (LDA) for dimensionality reduction.

9. 🔽 Dimensionality Reduction - Additional Techniques

   • Other techniques for dimensionality reduction.

10. 🖥️ SVM

    • Support Vector Machines (SVM) for classification and regression tasks.

11. 🧠 Perceptron and M&P

    • Perceptron and McCulloch-Pitts (M&P) for neural network-based learning.

12. 📈 MLP (Multilayer Perceptron)

    • Understanding Multilayer Perceptron (MLP) in neural network architectures.

13. 🤖 Autoencoder

    • Understanding autoencoder models in machine learning.

14. 📈 RBF

    • Radial Basis Function (RBF) networks for machine learning tasks.

15. 🌳 Decision Trees - ID3

    • Introduction to ID3 (Iterative Dichotomiser 3) decision tree algorithm.

16. 🌳 Decision Trees - C4.5

    • Understanding C4.5 decision tree algorithm.

17. 🌳 Decision Trees - CART

    • Classification and Regression Trees (CART) algorithm.

18. 🌲 Random Forest

    • Introduction to Random Forest algorithm.

19. 👥 Ensemble Learning - Boosting

    • Understanding boosting algorithms for ensemble learning.

20. 👥 Ensemble Learning - Bagging

    • Introduction to bagging techniques for ensemble learning.

21. 🕹️ Reinforcement Learning

    • Basics of reinforcement learning and its applications.

22. 📸 Convolutional Neural Networks (CNN)

    • Introduction to CNNs and their applications in image recognition.

23. 🧠 Long Short-Term Memory (LSTM)

    • Understanding LSTM networks for sequential data analysis.

• Each topic will be covered in detail through lectures, practical sessions, and assignments.
• Hands-on coding exercises will be provided to reinforce theoretical concepts.
• The course aims to equip students with practical skills in implementing and evaluating machine learning algorithms.

📢 We hope you find this course informative and engaging! Feel free to reach out to Dr. Aliyari or the teaching staff for any queries or assistance throughout the course.

🚀 Happy learning!

You'll find a collection of code examples in the code-samples directory. These examples will help you understand key machine learning concepts and techniques.

We have included a series of assignments in the assignments directory. These assignments are designed to reinforce your learning and provide practical experience. Please submit your completed assignments according to the guidelines provided in each assignment's README.

The tutorials directory contains step-by-step tutorials on various machine learning topics. These tutorials will guide you through implementing algorithms and solving real-world problems.

In the resources directory, you'll find supplementary materials such as lecture slides, reference guides, and recommended reading lists.

To get started with the course, follow these steps:

1. Clone or fork this repository to your local machine.
2. Review the course materials in the respective directories.
3. Complete the assignments and projects as instructed.
4. Engage in discussions and ask questions in the course's Issues section.
5. Stay updated with course announcements and updates by watching this repository.

We welcome contributions from students and the broader community. If you find issues, want to suggest improvements, or have your own machine learning projects to share, please open an issue or submit a pull request. Refer to our Contribution Guidelines for more details.

Please review and adhere to our Code of Conduct to create a respectful and inclusive learning environment for everyone.

This course repository is open-source and available under the MIT License. You are free to use, modify, and share the content, but please provide proper attribution.

If you have questions or need assistance, you can reach out to the course instructor:

• Instructor:

This section showcases the avatars of the main collaborators on this project. Click on any avatar to visit their GitHub profile and see their contributions.

MJAHMADEE	msinamsina	Ardawanism	shining0611armor	sepidehetaati
ArmanFz	dorsamgh	ErfanY2AJ	hassanyousefzade	ghanbarzadeh
rezamoradi

To cite this resource in a publication or academic paper, you can use the following BibTeX entry:

@misc{MJAHMADEE2024,
  author = {K. N. Toosi University of Technology},
  title = {Machine Learning Course 2024},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/MJAHMADEE/MachineLearning2024W}}
}

For those who are using the IEEE citation style, the following format can be used:

K. N. Toosi University of Technology, "Machine Learning Course 2024," GitHub repository, 2024. [Online]. Available: https://github.com/MJAHMADEE/MachineLearning2024W

🚀 Happy learning!