Human Centered AI: How AI Thinks

Course Description

This course provides a comprehensive introduction to the design, development, and responsible use of modern artificial intelligence models, with a particular focus on large language models (LLMs). Students will learn the core concepts and technologies underlying these systems, including their layered architectures, in-context learning, prompt engineering, retrieval-augmented generation (RAG), and fine-tuning. Through hands-on activities, students will build a simple language model, experiment with creating functional AI tools, and critically analyze them for ethical, alignment, and explainability challenges.

Key topics include AI alignment, interpretability, and explainability, with emphasis on methods that make AI decision-making transparent and its logic accessible to users. The course also explores human oversight techniques, incorporating mechanisms such as reinforcement learning (RL) and reinforcement learning with human feedback (RLHF).

Key Topics

• Python Bootcamp
• Introduction to AI & Machine Learning
• Data Analysis and Visualization Tools
• Classification
• Regression
• Neural Networks and Deeplearning
• Project Work
• Final Presentation
  

Prerequisites/Skills Needed:

A foundational understanding of computer programming concepts, including variables, loops, and functions.

• Basic knowledge of data structures such as lists, arrays, or dictionaries.
• An introduction of artificial intelligence