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title: "Machine Learning Fundamentals" course: "CS420: Introduction to Machine Learning" author: "Alex Chen" date: "2024-03-15" instructor: "Dr. Sarah Miller" department: "Computer Science" tags:

• machine learning
• algorithms
• data science
• neural networks

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Quick Reference

Key Terminology

• Machine Learning: A subset of AI that enables systems to learn from data
• Supervised Learning: Learning with labeled training data
• Unsupervised Learning: Learning from unlabeled data
• Neural Network: A computing system inspired by biological neural networks

Important Equations

1. Linear Regression: y = mx + b

2. Sigmoid Function: σ(x) = 1 / (1 + e^(-x))

3. Cost Function: J(θ) = (1/2m) ∑(h_θ(x) - y)²

Learning Objectives

By the end of this course, you should be able to:

1. Understand fundamental ML concepts
2. Implement basic ML algorithms
3. Evaluate model performance
4. Apply ML to real-world problems

Key Concepts

Types of Machine Learning

1. Supervised Learning

• Classification
• Regression
• Examples:
  • Spam detection
  • House price prediction

2. Unsupervised Learning

• Clustering
• Dimensionality reduction
• Examples:
  • Customer segmentation
  • Feature extraction

3. Reinforcement Learning

• Policy learning
• Reward-based learning
• Examples:
  • Game AI
  • Robot navigation

Training Process

# Example: Simple Linear Regression
import numpy as np

class LinearRegression:
    def __init__(self, lr=0.01):
        self.lr = lr
        self.weights = None
        self.bias = None
    
    def train(self, X, y, epochs=100):
        n_samples = len(X)
        self.weights = np.zeros(X.shape[1])
        self.bias = 0
        
        for _ in range(epochs):
            y_pred = np.dot(X, self.weights) + self.bias
            
            # Update weights
            dw = (1/n_samples) * np.dot(X.T, (y_pred - y))
            db = (1/n_samples) * np.sum(y_pred - y)
            
            self.weights -= self.lr * dw
            self.bias -= self.lr * db

Examples & Practice Problems

Example 1: Classification

Problem: Email Spam Classification

• Features: Word frequency, sender info
• Labels: Spam/Not spam
• Method: Naive Bayes classifier

Example 2: Regression

Problem: House Price Prediction

• Features: Size, location, age
• Target: Price
• Method: Multiple linear regression

Common Mistakes to Avoid

1. Overfitting

   • Using too complex models
   • Not using regularization
   • Solution: Cross-validation

2. Data Leakage

   • Including test data in training
   • Not properly splitting data
   • Solution: Proper data handling

3. Feature Selection

   • Using irrelevant features
   • Not normalizing data
   • Solution: Feature engineering

Review Questions

1. What is the difference between supervised and unsupervised learning?
2. Explain the concept of overfitting and how to prevent it.
3. What is gradient descent and how does it work?
4. Describe the bias-variance tradeoff.

Additional Resources

Books

1. "Introduction to Machine Learning" by Ethem Alpaydin
2. "Pattern Recognition and Machine Learning" by Christopher Bishop

Online Courses

1. Coursera: Machine Learning Specialization
2. edX: CS50's Introduction to AI with Python

Tools & Libraries

• Python: scikit-learn, TensorFlow, PyTorch
• R: caret, mlr
• Julia: MLJ.jl

Study Tips

1. Practice Implementation

   • Code basic algorithms from scratch
   • Use standard libraries for complex models
   • Participate in Kaggle competitions

2. Understand Mathematics

   • Review linear algebra
   • Study calculus fundamentals
   • Learn probability theory

3. Work with Real Data

   • Use public datasets
   • Create personal projects
   • Document your findings

Notes & Questions

Important Points to Remember

• Review gradient descent algorithms
• Practice implementing neural networks
• Study evaluation metrics

Questions for Next Class

1. How to handle imbalanced datasets?
2. Best practices for model deployment?
3. When to use different optimization algorithms?

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Last Updated: 2024-03-15 Version: 1.0