BACII Applied AI Final project allows students to demonstrate their understanding in AI and its application and be able to apply it in thier own work or project. 

Submission must include the following files
1. presentation slides [you will use this for your final 10 mins presentation, including appendix, reference etc.]
2. video demo
3. live demo
4. Reference files (if has)

Presentation must include the following topics (but not limit to, you can add what you think related to your works)

- Inspiration

- What it does

- How our team built it e.g. software architecture, software development, problem statement, data collection, data pipeline, model development, model deployment, launch etc.

- Challenges our team ran into

- Accomplishments that I'm proud of

- What I learned

- Try it out

Example (but for your submission please provide as much detail as you can)
Inspiration

Nowadays, restaurant industry offers a wide range of dishes. People cannot find the right dishes without trial and error.

Our application offers customized restaurant menus for individual restaurant patrons to maximally enrich their experience by recommending dishes and drinks that each of them is likely to enjoy most.

We use Ionic for the front end/mobile-app development, and Django RESTful for the backend. Our recommender engine is based on a hybrid approach between collaborative filtering and dish-based recommendation with uses of mechanisms to garner patrons’ implicit feedback from application usages to guide the recommender engine. We bootstrap our recommender with data from Point-of-Sale machines from restaurants.

Varieties in different recipes, tastes, cooking styles, and presentations make it very hard to profile a dish.

We have built a prototype that has (almost) all functionalities of the entire system using real data form PoS machines in just two-and-a-half days. We also formulated some new ideas about how we might better profile dishes.

1. It is quite hard to profile dishes efficiently.
2. Using and getting implicit feedback well is quite a challenge that requires thinking about the whole system holistically. ## What's next for Smart Menu
3. Improve the recommender engine in the prototype.
4. Market/deploy it in restaurants to garner more data.

•  GitHub Repo

Instructor:
 Dr. Warodom Khamphanchai, CEO AltoTech

TA
Mr. Jirayut Chatphet, Senior Data Scientist AltoTech

• Final Project Criteria
  1. 40% on how you apply AI into your project (e.g. ML, DL algorithms and applications) 2. 30% presentation skills 3. 20% on Software Architecture Design and Development 4. 10% Business Model