| Week | Lecture Topic | Homework (Datacamp) |
|---|---|---|
| PART I: Artificial Intelligence Overview | ||
| 1 1/28 | Course Introduction – The instructor, the outline, classroom conduct, academic integrity, attendance, and grading policy. What is Artificial Intelligence (AI)? – Different Views and Approaches to AI – AI vs ML vs DL – Predictive vs Generative AI – AI in Industry | Course: Introduction to AI for Work – Chapter 1: The Foundations of Artificial Intelligence – Chapter 2: AI as a Force Multiplier for Productivity – Chapter 3: Mastering AI Collaboration – Chapter 4: Using AI Responsibly and Beyond |
| 2 2/4 | Generative AI and Prompt Engineering – Key Concepts in Prompt Engineering – Best Practices for Effective Prompt Engineering – Methodologies | Course: Understanding ChatGPT – Chapter 1 – Interacting with ChatGPT – Chapter 2- Adopting ChatGPT |
| 3 2/11 | Overview of Machine Learning – What is Supervised Learning? – What is Unsupervised Learning? – What is Reinforcement Learning? – ML vs. Data Science vs. Other Fields | Course: Understanding Machine Learning – Chapter 1 – What is Machine Learning? – Chapter 2- Machine Learning Models – Chapter 3- Deep Learning |
| 4 2/18 | EXAM I (AI, ChatGPT, Prompt Engineering) Large Language Models (LLMs) Concepts | Course: Large Language Models (LLMs) Concepts – Chapter 1- Introduction to Large Language Models (LLM) – Chapter 2 – Building Blocks of LLMs – Chapter 3- Training Methodology and Techniques – Chapter 4 – Concerns and Considerations |
| 5 2/25 | Developing Generative AI Applications and Agents | Course: Generative AI Concepts – Chapter 1- Introduction to Generative AI – Chapter 2- Developing Generative AI Models – Chapter 3- Using AI Models and Generated Content Responsibly – Chapter 4- Getting Ready for the Age of Generative AI |
| 6 3/4 | AI Ethics and Responsibility – Ownership and Copyrights – Regulations | Course: AI Ethics – Chapter 1- Approaching AI Ethics – Chapter 2- Below the Surface: AI Ethics – Chapter 3- The Way Forward: AI Ethics AI Fundamentals Assessment |
| PART II: Machine Learning | ||
| 7 3/11 | EXAM II (ML, LLMs, GenAI, Ethics) Introduction to Supervised Learning – Regression | Course: Supervised Learning with scikit-learn |
| 8 3/18 | Introduction to Supervised Learning – Classification | Course: Supervised Learning with scikit-learn |
| 9 3/25 | Introduction to Unsupervised Learning – Cluster analysis with K-Means – Hierarchical clustering | Course: Unsupervised Learning in Python |
| 10 4/1** | ** No Classes. Enjoy Spring Break! | |
| 11 4/8** | ** No Classes. Enjoy Spring Break! | |
| 12 4/15 | EXAM III (Supervised and Unsupervised Learning) Introduction to Deep Learning: | Course: Introduction to Deep Learning with PyTorch |
| 13 4/22 | More about Deep Learning | Course: Introduction to Deep Learning with PyTorch |
| 14 4/29 | Intro to Deep Reinforcement Learning | Reinforcement Learning with Gymnasium in Python |
| PART III: Final Project | ||
| 15 5/6 | EXAM IV (Deep Learning, Reinforcement Learning) Final Project | TBD |
| 16 5/13 | Final Project | TBD |
| 17 5/20 | Final Project Presentation | |