AI Algorithm Limitations Course Syllabus

Overview: This course provides a comprehensive exploration of the limitations, risks, and ethical considerations inherent in AI algorithms. Designed for beginners, it emphasizes conceptual understanding over technical coding, helping learners critically assess AI systems. The course spans approximately 15–18 hours across six modules, combining case studies, guided discussions, and reflective exercises to build awareness of AI's boundaries and societal impacts.

Module 1: Foundations of Computing & Algorithms

Estimated time: 4 hours

• Introduction to key concepts in foundations of computing & algorithms
• Case study analysis with real-world examples
• Interactive discussion on algorithmic thinking and its limits
• Understanding the role of assumptions in algorithm design

Module 2: Neural Networks & Deep Learning

Estimated time: 3 hours

• Review of neural network fundamentals and limitations
• Discussion of best practices and industry standards
• Case study analysis with real-world examples
• Exploration of overfitting, generalization, and data dependency

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Case study analysis with real-world examples
• Hands-on exercises applying AI system design & architecture techniques
• Understanding trade-offs in scalability, accuracy, and interpretability

Module 4: Natural Language Processing

Estimated time: 2 hours

• Introduction to key concepts in natural language processing
• Review of tools and frameworks commonly used in practice
• Discussion of NLP biases and contextual misunderstandings

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

• Introduction to key concepts in computer vision & pattern recognition
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Exploring limitations in image recognition and bias in training data

Module 6: Deployment & Production Systems

Estimated time: 3 hours

• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Guided project work with instructor feedback
• Evaluating real-world performance and failure modes of deployed AI systems

Prerequisites

• Familiarity with basic computing concepts
• No prior programming experience required
• Interest in AI ethics and responsible technology use

What You'll Be Able to Do After

• Identify inherent limitations and risks in AI algorithms
• Recognize sources of bias and error in machine learning models
• Evaluate AI systems critically for reliability and fairness
• Communicate the boundaries of AI performance to non-technical stakeholders
• Support ethical AI deployment through informed decision-making