| Week | Lecture Topic | Homework (Datacamp) |
|---|---|---|
| PART I: Artificial Intelligence Overview | ||
| 1 8/29 | 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: Understanding Artificial Intelligence – Chapter 1 – What is Artificial Intelligence (AI)? – Chapter 2- Tasks AI can solve – Chapter 3 – Harnessing AI in Organizations – Chapter 4- The human side of AI |
| 2 9/5 | Generative AI and Prompt Engineering – Key Concepts in Prompt Engineering – Best Practices for Effective Prompt Engineering – Methodologies | Course: Introduction to ChatGPT – Chapter 1 – Interacting with ChatGPT – Chapter 2- Adopting ChatGPT |
| 3 9/12 | 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 9/19 | 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 9/26 | 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 10/3* 10/10 | * No class on 10/3 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 |
| PART II: Machine Learning | ||
| 7 10/17 | EXAM II (ML, LLMs, GenAI, Ethics) Introduction to Supervised Learning – Regression | Course: Supervised Learning with scikit-learn |
| 8 10/24 | Introduction to Supervised Learning – Classification | Course: Supervised Learning with scikit-learn |
| 9 10/31 | Introduction to Unsupervised Learning – Cluster analysis with K-Means – Hierarchical clustering | Course: Unsupervised Learning in Python |
| 10 11/7 | EXAM III (Supervised and Unsupervised Learning) Introduction to Deep Learning: | Course: Introduction to Deep Learning with PyTorch |
| 11 11/14 | More about Deep Learning | Course: Introduction to Deep Learning with PyTorch |
| 12 11/21 | Intro to Deep Reinforcement Learning | Reinforcement Learning with Gymnasium in Python |
| 13 11/28** | **No class. Happy Thanks Given!! | |
| PART III: Disconnected Study | ||
| 14 12/5 | EXAM IV (Deep Learning, Reinforcement Learning) Final Project | TBD |
| 15 12/12 | Final Project | TBD |
| 16 12/19 | Final Exam and Final Project Presentation | |
The OpenLab at City Tech:A place to learn, work, and share
The OpenLab is an open-source, digital platform designed to support teaching and learning at City Tech (New York City College of Technology), and to promote student and faculty engagement in the intellectual and social life of the college community.
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