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

Week 1: Supervised Learning, Overfitting & The ML Pipeline

Build your ML intuition: supervised learning algorithms, the bias-variance tradeoff, regularization strategies, and the complete model evaluation pipeline.

University of America

AI102 — Lecture 1 · BSc Y1

🎬 CC Licensed Lecture

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

🎯 Learning Objectives

Implement linear regression with gradient descent from scratch
Understand and diagnose bias-variance tradeoff
Apply regularization (L1, L2) to prevent overfitting
Build and evaluate a complete ML pipeline

Topics Covered This Lecture

Supervised Learning Framework

Linear & Polynomial Regression

Regularization: L1 & L2

Model Evaluation: CV, Metrics

📖 Lecture Overview

This first lecture establishes the foundational framework for Introduction to Machine Learning. 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
        Build your ML intuition: supervised learning algorithms, the bias-variance tradeoff, regularization strategies, and the complete model evaluation pipeline. 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: Supervised Learning Framework, Linear & Polynomial Regression, Regularization: L1 & L2, Model Evaluation: CV, Metrics. 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 AI102
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: Supervised Learning Framework and Linear & Polynomial Regression. 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

AI102 Project 1: From Scratch ML Pipeline

Implement linear regression, logistic regression, and k-NN entirely from scratch in NumPy. Compare against scikit-learn implementations. Evaluate on 3 UCI datasets.

NumPy implementations of 3 algorithms
Correctness verification against scikit-learn
Performance comparison on 3 datasets
Written analysis of algorithm trade-offs

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

Derive the normal equation for linear regression. When is it preferable to gradient descent?

Analysis Short Answer

Explain L2 regularization. Show mathematically why it shrinks weights toward zero.

Applied Code / Proof

What is the difference between a parametric and non-parametric model? Give examples of each.