Instructor: Berkay Aydin
Email: baydin2@gsu.edu
Course Webpage: this webpage or can be reached via iCollege
Office Location: 25 Park Pl NE - Room 720
Credit Hours: 4.0 hours
Description: This course offers a rigorous introduction to the theory, engineering, and societal dimensions of artificial intelligence and machine learning. Rather than treating AI as a narrow technical discipline, the course situates it within a broader intellectual landscape, examining how intelligent systems are conceived, built, scaled, and deployed, and what their growing presence means for economies, industries, and the environment. Students will progress through the core concepts of machine learning and AI, the infrastructure and toolchains that power modern AI systems, and the latest developments in applied AI engineering including foundation models and autonomous agents. The course closes by examining the real-world impact of AI on labor markets and global energy consumption, encouraging students to think critically about the systems they build and the world those systems operate in. Throughout the semester, students will engage with foundational ideas alongside current developments in the field, exploring concepts that span the full lifecycle of AI systems from design and training through deployment and societal impact. By the end of the course, students will have the conceptual vocabulary, technical understanding, and critical perspective needed to participate meaningfully in one of the most transformative technological transitions of our time.
Objectives: Upon successful completion of this course, students will:
Demonstrate a broad understanding of artificial intelligence and machine learning, including the diversity of learning paradigms, the roles and responsibilities within the AI profession, and the economic and environmental context in which AI systems operate.
Explain and evaluate the infrastructure and engineering decisions that underpin modern AI systems, including the design of training and inference pipelines, the use of embedding spaces and vector retrieval systems, and the trade-offs involved in building and scaling AI at production.
Apply contemporary AI engineering concepts and patterns, including retrieval-augmented generation, autonomous agents, and foundation model integration, to reason about the design and architecture of real-world AI applications.
Critically assess the broader societal implications of AI systems, including their impact on labor markets, energy consumption, and economic structures, and articulate the responsibilities that come with building and deploying such systems.
Demonstrate the ability to engage with current developments in the field, synthesizing ideas across technical, economic, and societal dimensions to form well-reasoned perspectives on the trajectory of artificial intelligence.
Topics Covered:
Stream 1: Core ML/AI
What is AI: definitions, history, and roles in the field
The AI economy and systems landscape
Learning paradigms I: supervised, unsupervised, and semi-supervised learning
Learning paradigms II: self-supervised, reinforcement, and generative vs predictive learning
Stream 2: AI Infrastructure & Training
AI toolchains and development environments
Training system design
Inference system design
Model optimization
Embedding spaces and similarity search
Vector databases and retrieval systems
Scalability: distributed systems, infrastructure, cost, and reliability
Data engineering for AI
Stream 3: Contemporary AI Engineering
Foundation models and prompt engineering
Retrieval-augmented generation (RAG)
AI agents: concepts and architecture
Fine-tuning and preference learning
Multimodal systems and production AI system design
Stream 4: AI Impact
Economic and labour impact of AI
Environmental and energy impact of AI
Grade CSc 4816 CSc 6816
A+ [100, ∞) [100, ∞)
A [95, 100) [96, 100)
A- [90, 95) [92, 96)
B+ [85, 90) [88, 92)
B [80, 85) [83, 88)
B- [75, 80) [79, 83)
C+ [70, 75) [75, 79)
C [65, 70) [70, 75)
C- [60, 65) [66, 70)
D [50, 60) [60, 66)
F [0, 50) [0, 60)