5-Week Syllabus with Activities: Introduction to Recommendation Systems & Generative AI for Personalized Suggestions
This course includes theory, hands-on coding, and engaging activities to reinforce learning.
📅 Week 1: Introduction to Recommendation Systems
Objective: Understand the fundamentals and different types of recommendation systems.
Topics:
🔹 What is a Recommendation System?
🔹 Content-Based, Collaborative Filtering, and Hybrid Approaches
🔹 Challenges: Cold Start, Data Sparsity, Scalability
🔹 Evaluation Metrics: Precision, Recall, RMSE, NDCG
🔍 Activities:
✅ Case Study Analysis: Analyze how Netflix, Spotify, and Amazon use recommendation systems.
✅ Group Discussion: Debate the ethical concerns of recommendation systems (filter bubbles, bias, privacy).
✅ Mini Coding Challenge: Implement a simple recommendation system using Python lists and dictionaries.
📅 Week 2: Content-Based Filtering
Objective: Learn to recommend items based on user preferences and item similarities.
Topics:
🔹 Feature Engineering: TF-IDF, Word Embeddings
🔹 Cosine Similarity, Euclidean Distance, Pearson Correlation
🔹 Strengths & Weaknesses of Content-Based Filtering
🔍 Activities:
✅ Paper Discussion: Read & summarize a research paper on Content-Based Filtering.
✅ Hands-on Coding: Build a movie recommender using TF-IDF and cosine similarity (Jupyter Notebook).
✅ Interactive Quiz: Test understanding of similarity measures.
📅 Week 3: Collaborative Filtering & Hybrid Models
Objective: Learn user-based and item-based collaborative filtering and hybrid approaches.
Topics:
🔹 User-Based vs Item-Based Collaborative Filtering
🔹 Matrix Factorization: SVD, ALS
🔹 Hybrid Systems: Combining CF & Content-Based
🔍 Activities:
✅ Role-Playing Exercise: Simulate a collaborative filtering system where participants recommend books based on others' preferences.
✅ Hands-on Coding: Implement collaborative filtering using the Surprise Library.
✅ Kaggle Competition Simulation: Predict user preferences in a real dataset.
📅 Week 4: Generative AI for Recommendation Systems
Objective: Use Generative AI (GPT, VAEs, GANs) for enhanced recommendations.
Topics:
🔹 How GPT & Transformers Enhance Recommendations
🔹 Generating User Profiles with AI
🔹 Case Study: AI-Powered Personalized Content
🔍 Activities:
✅ AI-Powered Text Generation: Use OpenAI's GPT to generate personalized content recommendations.
✅ Hands-on Coding: Build an NLP-based recommendation system with Hugging Face Transformers.
✅ AI Ethics Discussion: Debate the risks of AI in personalization (bias, manipulation).
📅 Week 5: MLOps for Recommendation Systems
Objective: Learn how to deploy and maintain scalable recommendation systems.
Topics:
🔹 MLOps Pipeline: Data Processing, Model Training, Monitoring
🔹 Deployment Strategies (Batch vs Real-time)
🔹 Scalability Techniques: Caching, Distributed Computing
🔍 Activities:
✅ DevOps Role Play: Teams simulate deploying a recommendation system and handling failures.
✅ Hands-on Coding: Deploy a recommendation system with Flask & Docker.
✅ Peer Review: Evaluate classmates' projects & give feedback.
🎯 Final Project: Build & Deploy Your Own AI-Powered Recommendation System!
Deliverables:
✅ Implement content-based, collaborative filtering, or hybrid models.
✅ Integrate Generative AI for personalization.
✅ Deploy & present findings.
This structure ensures learning by doing and real-world application. Would you like additional resources, such as datasets or further readings? 🚀