Columbia: Artificial Intelligence (AI) Course Syllabus

Overview: This advanced Artificial Intelligence course from Columbia University on edX offers a rigorous, academically grounded exploration of core AI concepts and theoretical foundations. Designed for learners with prior technical experience, the program spans six modules covering computing fundamentals, deep learning, AI system design, natural language processing, computer vision, and deployment practices. With a strong emphasis on problem-solving and real-world applications, the course requires approximately 15–20 hours of study and includes assessments, hands-on exercises, and a final project. Ideal for students and professionals aiming to build scalable, intelligent systems.

Module 1: Foundations of Computing & Algorithms

Estimated time: 2.5 hours

• Introduction to key concepts in foundations of computing & algorithms
• Discussion of best practices and industry standards
• Case study analysis with real-world examples

Module 2: Neural Networks & Deep Learning

Estimated time: 3.5 hours

• Introduction to key concepts in neural networks & deep learning
• Hands-on exercises applying neural networks & deep learning techniques
• Case study analysis with real-world examples
• Assessment: Quiz and peer-reviewed assignment

Module 3: AI System Design & Architecture

Estimated time: 4 hours

• Introduction to key concepts in AI system design & architecture
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 4: Natural Language Processing

Estimated time: 1.5 hours

• Introduction to key concepts in natural language processing
• Hands-on exercises applying natural language processing techniques
• Case study analysis with real-world examples
• Assessment: Quiz and peer-reviewed assignment

Module 5: Computer Vision & Pattern Recognition

Estimated time: 3 hours

• Introduction to key concepts in computer vision & pattern recognition
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Module 6: Deployment & Production Systems

Estimated time: 2 hours

• Introduction to key concepts in deployment & production systems
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice

Prerequisites

• Strong foundation in mathematics (linear algebra, probability, and calculus)
• Programming experience in Python or a similar language
• Familiarity with basic algorithms and data structures

What You'll Be Able to Do After

• Design and implement AI-powered applications for real-world use cases
• Apply computational thinking to solve complex engineering problems
• Understand transformer architectures and attention mechanisms
• Evaluate model performance using appropriate metrics and benchmarks
• Design algorithms that scale efficiently with increasing data