AI for You: Hands-On Designing Intelligent and Personalized Systems (Part2)

From tailored social media feeds to curated e-commerce suggestions and personalized news, artificial intelligence powers recommender systems that shape our daily lives. AI for You is an intermediate course that equips participants with basic Python and AI knowledge to design intelligent, personalized recommender systems (RecSys) using a case study approach. 

Explore how AI algorithms deliver customized experiences in domains like healthcare, visual arts, and shopping, progressing from traditional discriminative techniques to cutting-edge generative AI, including LLMs, Retrieval-Augmented Generation (RAG), and LangChain for interactive systems. With a focus on computational methods, human-centered design, and ethical AI, participants will implement and evaluate RecSys through hands-on coding. 

Delivered over an intensive week with morning theory sessions and afternoon coding sessions, the course includes group projects, advanced demos, and immersive art therapy prizes for top teams, blending technical depth with practical applications

This course will cover:

• Introduction to Personalization and Recommender Systems (analyzing real-world implementations in social media, e-commerce, and news feeds)
• Collaborative Filtering, Content-Based Filtering, and Hybrid Methods (leveraging AI for user-item interactions)
• Computational Methods for Designing Recommender Systems
• Introduction to Generative AI for Recommender Systems (advanced techniques for synthesizing recommendations)
• Generative Adversarial Networks (GANs) (adversarial training for realistic suggestion generation)
• Retrieval-Augmented Generation (RAG) for Enhanced Personalization (integrating external knowledge bases)
• Latest Advances in LLMs for Personalization (e.g., Memory-Assisted LLMs for long-term user modeling, Reasoning Graphs for complex inference)
• Introduction to Reinforcement Learning (RL) for Recommender Systems (optimizing AI through trial and error)
• Human-Centered Recommendation Systems (integrating user feedback into AI architectures)
• Evaluating Recommender Systems (rigorous assessment techniques)
• LangChain and Agentic AI for Interactive Recommendations (orchestrating AI chains for responsive systems)

By the end of the course, participants should be able to:

• Develop foundational knowledge of Recommender Systems (RecSys), including both discriminative and generative approaches
• Understand a wide variety of RecSys algorithms and their application across diverse domains, including healthcare and visual arts
• Design and evaluate human-centred RecSys, incorporating principles of user experience, ethical AI, and interdisciplinary considerations
• Apply latest advancements in Generative AI, LLMs, RAG, and LangChain for building intelligent, personalized, and interactive recommender systems through coding and implementation

Intensive week format: Theory sessions in the morning (in-depth lectures with technical examples) and hands-on activities in the afternoon (coding exercises in Google Colab, implementation of algorithms, group projects). Participants will write and debug Python code for RecSys models.