📅 2025-01-29 — Session: Developed Academic Productivity and ML Course Workflows

🕒 00:20–02:50
🏷️ Labels: Academic Productivity, Machine Learning, Workflow Design, Ai Integration, N8N Automation
📂 Project: Teaching
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance academic productivity and machine learning education through structured workflow design and course development strategies.

Key Activities

  • Pre-Queries for Academic Productivity Workflow: Planned pre-queries to enhance AI agent effectiveness in academic paper drafting.
  • Query Routing Integration: Developed strategies for query routing within academic workflows using metadata and semantic approaches.
  • DeepSeek-V3 Language Model Overview: Summarized architecture and performance metrics of the DeepSeek-V3 model.
  • Machine Learning Course Structure: Outlined comprehensive ML course covering foundational to advanced topics.
  • Workflow Design for Course Material Creation: Designed workflows for generating teaching materials and integrating AI agents.
  • n8n Automation Workflows: Developed workflows for academic paper summarization and PDF text extraction using n8n.

Achievements

  • Created a structured plan for integrating pre-queries and query routing in academic workflows.
  • Developed a comprehensive ML course structure, integrating foundational and advanced topics.
  • Designed advanced workflows for educational content management using AI and n8n.

Pending Tasks

  • Further integration of AI agents in course material creation.
  • Testing and refinement of n8n workflows for academic productivity.

Summary

This session successfully developed workflows and course structures aimed at enhancing academic productivity and machine learning education, leveraging AI and automation tools like n8n.