🤖 Artificial Intelligence Week 1 of 14 BSc · Y1 ⏱ ~50 min

Week 1: History, Philosophy & Core AI Paradigms

Survey the full AI landscape: from symbolic logic and rule-based systems to modern deep learning — understanding AI's history, philosophy, and future direction.

University of America

AI101 — Lecture 1 · BSc Y1

🎬 CC Licensed Lecture

0:00 / —:—— 📺 MIT OpenCourseWare (CC BY-NC-SA)

🎯 Learning Objectives

Trace the history of AI from Turing to the transformer era
Distinguish symbolic, connectionist, and hybrid AI approaches
Define intelligent behavior and the Turing Test
Map the current AI research landscape and open problems

Topics Covered This Lecture

AI History: Symbolic to Neural

Turing Test & Intelligent Behavior

Symbolic AI vs Machine Learning

The Modern AI Landscape

📖 Lecture Overview

This first lecture establishes the foundational framework for Principles of Artificial Intelligence. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Survey the full AI landscape: from symbolic logic and rule-based systems to modern deep learning — understanding AI's history, philosophy, and future direction. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: AI History: Symbolic to Neural, Turing Test & Intelligent Behavior, Symbolic AI vs Machine Learning, The Modern AI Landscape. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for AI101
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: AI History: Symbolic to Neural and Turing Test & Intelligent Behavior. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

AI101 Project 1: AI System Comparison Analysis

Select two AI systems from different paradigms (e.g., an expert system and a neural network) that solve the same problem. Compare their architectures, strengths, limitations, and real-world performance.

Technical comparison report (8-10 pages)
Architecture diagrams for both systems
Benchmark comparison on identical inputs
Recommendations for hybrid approaches

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

What is the difference between strong AI (AGI) and weak AI (narrow AI)? Give examples of each.

Analysis Short Answer

Describe Searle's Chinese Room thought experiment. What does it argue about machine understanding?

Applied Code / Proof

Compare rule-based expert systems and neural networks on interpretability, data requirements, and adaptability.