ML project-based course for complete beginners

Let us publish an ML paper in Explainable AI

Course Overview: Explainable AI (XAI)

This comprehensive course on Explainable AI (XAI) is designed to equip participants with the knowledge and skills necessary to interpret and understand complex machine learning models.

Module Breakdown:

1. Module 0: Introduction: Learn about the team, instructor, course vision, prerequisites, and expected outcomes.
2. Module 1: Explainable AI (XAI): Overview of XAI, its need, applications, and interpretability techniques.
3. Module 2: LIME: In-depth understanding and practical implementation of LIME.
4. Module 3: SHAP: Introduction to SHAP values and their practical application.
5. Module 4: Counterfactuals/Adversarial Examples: Generate counterfactuals and defend against adversarial examples.
6. Module 5: Layer-wise Relevance Propagation (LRP): Theory and application of LRP in deep learning models.
7. Module 6: XAI for Images: Techniques and practical application of XAI in image data.
8. Module 7: XAI for Tabular Data: Methods and workshops on interpreting tabular data.
9. Module 8: XAI for Text: Explainability techniques in NLP and ethical considerations.
10. Module 9: XAI for Graph Neural Networks: Interpretability techniques for GNNs.
11. Module 10: XAI for LLMs: Techniques and ethical considerations for Large Language Models.
12. Module 11-17: Research and Paper Writing: Identify research problems, conduct literature reviews, write and finalize research papers.
13. Module 18: New Research Problems: Explore and brainstorm emerging research topics in XAI.
14. Module 19: Research Groups: Overview of leading XAI research institutions and networking opportunities.
15. Module 20: Career Paths: Career opportunities in XAI, job preparation, and interview workshops.

Participants will gain practical experience, theoretical knowledge, and insights into the latest research and career opportunities in the field of XAI.