This course regards the adoption of classical and novel AI-based technologies in the field of fashion.

Despite expectations, the field of fashion is challenging from an AI perspective, since it involves the development of models to achieve general-purpose tasks, such as Image Classsification, Clustering and Retrieval, but also Trend Forecasting, Sentiment analysis and many others. So, it is touched by different AI subfields, such as Computer Vision, Natural Language process and Time series analysis.

This course is devoted to students from both Computer Science and Fashion studies, who want to explore the classical and the novel paradigms applied to solve some of the most common tasks in the Fashion Field.

This is my first full-course at the University of Bologna, at the Department for Life Quality Studies and, to the best of my knowledge, the first open-source repository of a University Course on Artificial Intelligence For Fashion.

The main textboook adopted in this course is:

@book{luce2018artificial,
  title={Artificial intelligence for fashion: How AI is revolutionizing the fashion industry},
  author={Luce, Leanne},
  year={2018},
  publisher={Apress}
}

• Orange Data Mining
• Google Colab

Date	Topic	Professor	Slide	Practical material
14/03/2022	Introduction	Lorenzo Stacchio	00_intro.pdf	Not present
15/03/2022	Ai for fashion	Lorenzo Stacchio	01_intro_ai_fashion.pdf	Not present
21/03/2022 22/03/2022	Python programming	Lorenzo Stacchio	02_python_programming.pdf
28/03/2022	Data visualization and Orange	Alessia Angeli	03_visualization.pdf 03_visualization_orange.pdf
29/03/2022	Python and ML Classification with sklearn	Lorenzo Stacchio	04_python_classification.pdf
04/04/2022 05/04/2022	Machine learning classification with Orange	Lorenzo Stacchio	05_orange_classification.pdf
11/04/2022 12/04/2022	Professional vs customer photos visual relationship: a simple project with Orange	Lorenzo Stacchio	06_final_project.pdf

All the students projects will be included in project folder starting from the first Academic Year (2021/2022).

Per each Academic Year, the project will be described along with all the students who participated in it.