ENGR4450 - Applied Artificial Intelligence (AI) Tutorials

Welcome to the official repository for ENGR4450 - Applied Artificial Intelligence (AI), a course offered at Işık University. This repository contains six Jupyter notebooks (tutorials) prepared by Burak KILIC, the course assistant, under the supervision of Assoc. Prof. Ali Taner KUZU.

Course Tutorials

This repository contains the following tutorials, each focusing on key topics in applied artificial intelligence:

• 1_intro_to_scikit_learn-linear_models.ipynb
  An introduction to linear models and an overview of the scikit-learn library for AI and machine learning in Python.

• 2_linear_models.ipynb
  Detailed exploration of bias vs. variance tradeoff and underfitting vs. overfitting.

• 3_k_nearest_neighbors.ipynb
  Explanation and implementation of the K-Nearest Neighbors (KNN) algorithm.

• 4_support_vector_machines.ipynb
  A guide to Support Vector Machines (SVMs) for classification and regression tasks.

• 5_neural_networks.ipynb
  Introduction to artificial neural networks (ANNs), covering the basics of perceptrons and multi-layer perceptrons (MLPs).

Installation

To run these notebooks locally, follow the steps below:

1. Clone the repository

git clone https://github.com/burakai/ENGR4450.git
cd ENGR4450

2. Set up a virtual environment (using conda)

It is recommended to create a conda environment to isolate dependencies:

conda create -n engr4450 python=3.12
conda activate engr4450

Alternative: Using venv

You can also create a virtual environment using Python’s built-in venv module:

python -m venv env
source env/bin/activate   # On Windows use `env\Scripts\activate`

3. Install dependencies

All required dependencies are listed in the requirements.txt file. You can install them using pip:

pip install -r requirements.txt

4. Launch Jupyter Notebook

Once dependencies are installed, launch Jupyter Notebook to access the tutorials:

jupyter notebook

or use Jupyter Lab instead:

jupyter lab

This will open Jupyter in your default web browser. You can navigate to the notebooks and run them interactively.

Alternatively, you can click the button next to each notebook title to open it in Google Colab.

However, please be aware that Google Colab may not render some components, such as YouTube videos, as effectively.

Sources

The primary source of reference for the tutorials is the official scikit-learn documentation. Other sources have also been used where necessary, and appropriate references are provided within the relevant sections of the tutorials.

Authors

• Burak KILIC: Course Assistant, primary developer of the tutorials.
  Department: Mechanical Engineering
  Office: LMF 121
  Email: [email protected]

• Assoc. Prof. Ali Taner KUZU: Supervisor of the course at Işık University.
  Department: Mechanical Engineering
  Office: AMF 426
  Email: [email protected]

Usage and Feedback

You are welcome to use and adapt the content in these tutorials for your learning or teaching purposes, as long as you provide proper attribution to the author. Please ensure that any referenced material is properly credited.

If you notice any unreferenced content, mistakes, or have any feedback, feel free to contact me (Burak KILIC) or open an issue in this repository.

Contributions are welcome! Feel free to provide feedback or suggest improvements by contacting the authors or opening an issue in this repository.

License

This repository and its contents are licensed under the MIT License. You are free to use, modify, and distribute the materials in this repository for personal or educational purposes, provided that proper attribution is given to the original authors.

Conditions

1. Attribution: Any distributed or modified version of this work must include the original authors' names and the link to the repository.
2. No Warranty: The materials are provided "as-is," without any warranty of any kind, express or implied.

If you have any questions about the license or usage of this repository, please feel free to contact the authors.