Course syllabus

Academic Roadmap.

A public-facing deep dive from theoretical foundations to industrial implementation, bridging academic rigor and generative frontiers.

Topic 01

Introduction to GenAI and course structure

An editorial opening that frames generative AI, the semester arc, and the project-centered teaching method.

Probability Latest Spaces
  • Introduces the course logic and public-safe boundaries of the site.
  • Connects the GenAI landscape to the semester project format.
Attention Mechanisms Scalability
  • Language-model fundamentals and token prediction.
  • Transformer evolution and user-facing chat systems.

Topic 02

Large Language Models

Students move from language-model basics to the transformer era and study what is the attention, MOE, tokenization, RLHF for training the model.

Industrial insight

Guest lecture: AI agents

The course pivots toward tool use, coding, computer use agents, and multi-agent systems alongside an external industry perspective.

Topic 04

Prompt & Context engineering

The essential guideline for crafting the input for LLM to share the intent.

Fine-tuning Alignment
  • Instruction following by the model
  • Reasoning workflows for structured technical work.
Diffusion Vision
  • Multimodal model design and applications.
  • Vision-language interaction and generation.

Topic 05

Diffusion models and vision

Students examine multimodal systems, generative imaging, and the shift from text-only interfaces to richer model interaction.

Checkpoint

Mid-semester project review

One week is reserved for prototyping, tutor feedback, and aligning project scope with the final delivery phase.

auto_awesome

Core course philosophy

"We do not only teach how to prompt; we teach how to design systems around models so students can reason about utility, safety, and deployment together."

Topic 06

Practical deployment

The closing phase covers production-minded agentic systems, governance, open models, and the security & privacy.

RAG systems Agentic reasoning
  • Attack and defence for responsible AI
  • Open-source local AI model with license

Capstone achievement

Final presentation

  • Live demo or prototype walkthrough
  • Technical architecture documentation
  • Testing, safety, and reflection

Finale

Final presentation

Students showcase their end-to-end generative AI systems through demos, technical framing, and deployment reflection.