This program provides an in-depth overview of the history and practice of applying generative systems to creative tasks. Below is a comprehensive outline of what the course entails, along with exercises and homework assignments.

This program delves into the world of generative art and computational creativity, offering students a robust understanding of these concepts. You will explore various algorithm families from artificial intelligence, machine learning, and artificial life, all of which have been pivotal in generative processes.

The course material is enriched with numerous examples from past and contemporary creative practices, encompassing:

• Visual Art
• Music
• Poetry and Literature
• Performing Arts
• Design and Architecture
• Games
• Bioart
• Robotic Art

By the end of this program, you will:

• Understand the fundamentals of generative art and computational creativity.
• Gain knowledge of key algorithms used in generative processes.
• Apply these algorithms practically to create new generative pieces using the graphical programming language Max.
• Engage in philosophical and societal debates on the automation of creative tasks.

The lectures will cover:

• Definitions and history of generative art and computational creativity.
• Detailed exploration of AI, machine learning, and artificial life algorithms.
• Case studies from various creative fields.

You will engage in hands-on exercises using Max to implement algorithms and create generative art. Each exercise will build on the concepts discussed in lectures.

Homework will include:

• Developing original generative pieces.
• Critical analysis of existing generative works.
• Reflective essays on the philosophical and societal implications of automated creativity.

To get started, follow these steps:

1. Install Max: Download and install the graphical programming language Max from the official website.
2. Clone the Repository: Clone this repository to your local machine to access lecture materials, exercises, and assignments.

   git clone [repository URL]

3. Explore the Materials: Navigate through the folders to find lecture slides, exercise files, and homework assignments.

We welcome contributions to enhance the course material. Please follow the standard Git workflow for contributions:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes and push them to your fork.
4. Submit a pull request for review.

This program is licensed under the MIT License. See the LICENSE file for details.

For any questions or support, please contact the course instructor at [email address].

We hope you find this program enriching and inspiring. Happy learning!

/generative-systems-creative-tasks
│
├── /lectures
│   ├── lecture1.pdf
│   ├── lecture2.pdf
│   └── ...
│
├── /exercises
│   ├── exercise1
│   │   ├── instructions.md
│   │   └── ...
│   └── ...
│
├── /homework
│   ├── homework1
│   │   ├── instructions.md
│   │   └── ...
│   └── ...
│
└── README.md