Course Number
16:137:562
Credits
3
Prerequsites
You must have programming experience to take this course. (16:137:552) Python Methodologies, or the equivalent.
Semesters(s) Offered
Spring
Description
Learn the processes for specifying, designing, and launching for commercialization AI/deep learning products using available software platforms, with a detailed examination of one of the major tools. Work in teams throughout the semester to apply learned business and technical concepts to launch a virtual AI/deep learning product.
Course Objectives
Upon successful completion of the course, be able to:
- Discern traditional computational models from those used in AI and Machine Learning (ML)
- Have a greater understanding of Deep Learning methods within the context of ML
- Familiarity with popular Deep Learning Architectures such as CNN, RNN, RL, LSTM, GAN, Transformers, Autoencoder, Attention Networks
- Familiarity with Application Frameworks for Deep Learning such as: Python, Keras, TensorFlow, Jupyter Notebooks, Kaggle, etc.
- Understand the business framework needed to identify, develop and present a coherent plan for funding a business for the AI Product
Testimonials
Course Testimonials
"In the Applied AI course you actually get to code and learn about deep neural networks. This is a very valuable skill in the field of analytics." - Shivani Sethi, Analytics, MBS'20