Education Template — 10 slides — Theme: paper

Introduction to Machine Learning Lecture

A 10-slide lecture presentation introducing machine learning concepts, algorithms, and practical exercises for undergraduate students.

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Slide 1---
title: "Introduction to Machine Learning"
theme: paper

Slide 2# Introduction to Machine Learning
## Lecture 1 — Foundations and Core Concepts

Slide 3## Learning Objectives

By the end of this lecture, you will be able to:

1. Define machine learning and distinguish it from traditional programming
2. Identify the three main types of ML (supervised, unsupervised, reinforcement)
3. Understand the train/test/validate workflow
4. Run your first ML classifier in Python

Slide 4## What is Machine Learning?

- **Traditional programming**: Rules + Data → Output
- **Machine learning**: Data + Output → Rules (learned)
- **Key insight**: Instead of writing rules, we let the algorithm discover patterns in data

> "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." — Tom Mitchell

Slide 5## Types of Machine Learning

| Type | Input | Goal | Example |
|------|-------|------|---------|
| Supervised | Labeled data | Predict labels | Spam detection |
| Unsupervised | Unlabeled data | Find patterns | Customer segmentation |
| Reinforcement | Environment + rewards | Maximize reward | Game playing |

Slide 6## The ML Workflow

**Collect data**: Gather relevant, representative samples
**Prepare data**: Clean, normalize, split into train/test
**Choose model**: Select algorithm based on problem type
**Train model**: Feed training data, adjust parameters
**Evaluate**: Measure performance on test data
**Deploy**: Use the model in production

Slide 7## Core Concepts

- **Features**: Input variables the model learns from (e.g., age, income)
- **Labels**: Target variable to predict (e.g., "spam" or "not spam")
- **Training data**: Examples the model learns from
- **Test data**: Examples held out to evaluate performance
- **Overfitting**: Model memorizes training data, fails on new data
- **Underfitting**: Model too simple to capture patterns

Slide 8## Common Algorithms

| Algorithm | Type | Use Case | Complexity |
|-----------|------|----------|------------|
| Linear Regression | Supervised | Prediction | Low |
| Logistic Regression | Supervised | Classification | Low |
| Decision Trees | Supervised | Classification | Medium |
| K-Means | Unsupervised | Clustering | Low |
| Neural Networks | Supervised | Complex patterns | High |

Slide 9## Hands-On Exercise

**Dataset**: Iris flower classification (150 samples, 4 features, 3 classes)

**Goal**: Train a classifier to predict flower species from measurements

**Steps**:
1. Load the dataset with scikit-learn
2. Split into training and test sets (80/20)
3. Train a Decision Tree classifier
4. Evaluate accuracy on the test set

Slide 10## Key Takeaways

ML learns rules from data instead of being explicitly programmed
Supervised learning is the most common paradigm (labeled data → predictions)
Always evaluate on held-out test data to check generalization
Start simple (linear models) before trying complex approaches

Slide 11## Next Lecture

**Topic**: Supervised Learning Deep Dive
- Linear and logistic regression in detail
- Loss functions and optimization
- Feature engineering techniques
- Bias-variance tradeoff

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