ICSE70011.01 · 2026-2027 Academic Year · Semester 1
AI Design:
Introduction & Fundamentals
From Transformers and diffusion models to multimodal agents, inference systems, and AI hardware, build a complete path from principles to applications.
- Credits / hours
- 2 credits · 36 hours
- Language
- Bilingual
- Audience
- Graduate students
to
Application
- 01TransformTurn a problem into a computable representation
- 02ReasonUse models and evidence to test decisions
- 03BuildTurn the idea into a runnable, measurable system
Course overview
Learn to design AI systems, not only use models.
This graduate course introduces the principles and practice of AI design and software development. Topics include Transformers, diffusion, multimodal and psychology-informed agents, inference extensions, BitNet accelerators, and memory-free computing.
You will implement simplified models, call leading APIs, and connect theory, engineering constraints, and interdisciplinary questions through a capstone project.
View course detailsCourse projects
Start with a runnable minimum.
Model Router + RSL
Build an AI gateway that selects models by task, budget, latency, and risk, then improves through independently evaluated, reversible updates.
02AI for HardwareOptical Module Tuning
Use machine learning and experimental data to find optical-module settings that satisfy bit-error, power, temperature, and stability constraints.
03Physical AILiDAR 3D World Model
Build a traceable pipeline from LiDAR point-cloud collection and synchronization through labeling, quality control, and a 3D world-model evaluation.
Research directions
Turn course questions into research.
Paid research opportunities are open to undergraduate and graduate students, including part-time and summer formats.