Course Syllabus

Lecture videos available on YouTube (Link).

1. Introduction & Course Overview : Overview of the course objectives, expectations, and the role of AI in solving problems.
2. How ML Works: Introduction to machine learning concepts, including supervised learning and basic algorithms.
3. Regression and Classification: Regression and classification models and their use in predicting continuous and categorical outcomes.
4. Neural Networks: Part 1: Introduction to neural networks, including the basics of deep learning.
5. Neural Networks: Part 2: Code walkthrough of a simple image classification model
6. The Art of Creation (Generative AI): Overview of generative AI
7. LLM: LLM, transformers, tokens
8. Building products with LLM (part 1)
9. Building products with LLM (part 2)
10. AI Agents: Introduction to AI agents and their use in real-world applications.
11. Responsible AI: Discussion on ethical considerations, fairness and transparency in AI.
12. Capstone Project Presentations (Part 1): Students will present their capstone projects.
13. Capstone Project Presentations (part 2)
14. Wrap-Up: Recap and discussion on next steps in the field of AI.