Machine Learning for Everyone - Kylie Ying

Introduction

• Speaker: Kylie Ying
• Background: MIT, CERN, Free Code Camp; physicist, engineer
• Focus: Making machine learning accessible to beginners
• Topics Covered: Supervised learning, unsupervised learning, programming with Google CoLab

Supervised Learning

Overview

• Definition: Uses labeled inputs to train models to predict new outputs
• Types: Classification, Regression
• Datasets: Labeled data; input with corresponding output labels

Classification

• Binary Classification: Two classes - e.g., spam/not spam, cat/dog
• Multiclass Classification: More than two classes - e.g., multiple species
• Examples: Email spam filtering, sentiment analysis
• Model Types: K-nearest neighbors (KNN), Naive Bayes, Logistic Regression, Support Vector Machines (SVM), Neural Networks

Regression

• Linear Regression: Predicts continuous values - e.g., house prices
• Model: Linear relationship between input and output
• Evaluation Metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared

Unsupervised Learning

Overview

• Definition: Uses unlabeled data to find patterns or structure
• Types: Clustering, Dimensionality Reduction

Clustering

• K-means Clustering: Divides data into K clusters based on similarity
• Steps: Initialize centroids, assign points to nearest centroid, recompute centroids, repeat

Dimensionality Reduction

• Principal Component Analysis (PCA): Reduces dimensions while retaining most variance in the data
• Process: Projects data onto principal components, captures maximum variance

Example Dataset: Gamma Telescope Data

• Source: UCI Machine Learning Repository
• Objective: Predict type of particle (gamma or hadron)
• Features Collected: Length, width, size, asymmetry, etc.
• Programming Environment: Google CoLab
• Libraries Used: NumPy, Pandas, Matplotlib
• Steps: Import libraries, load dataset, preprocess (handle missing labels), convert class labels to numerical, analyze features, train test split, scale data, oversample for imbalance

Programming in Google CoLab

Initial Setup

• Libraries: Import NumPy, Pandas, Matplotlib, scikit-learn (for models), TensorFlow (for neural networks)
• Data Import: Load datasets using pandas, clean and preprocess data (handling missing values, scaling, encoding categorical variables)

Model Implementation

K-Nearest Neighbors (KNN)

• Process: Define a distance function, determine number of neighbors (K), assign labels based on majority class of nearest neighbors

Naive Bayes

• Concept: Uses Bayes’ theorem and assumes features are independent
• Process: Calculate posterior probabilities, determine class with highest posterior probability

Logistic Regression

• Concept: Fit data to a logistic curve (sigmoid function)
• Application: Binary classification tasks with continuous input features

Support Vector Machines (SVM)

• Concept: Find hyperplane that best separates different classes
• Characteristic: Sensitive to outliers, effective for high-dimensional space

Neural Networks

• Concept: Layers of interconnected nodes (neurons), weights adjusted through back propagation
• Implementation: Use TensorFlow to define, compile, and train neural network models
• Evaluation: Plot training history, evaluate on test data

Visualization and Analysis

Data Visualization

• Histograms: Compare distribution of features across classes
• Scatter Plots: Visualize feature relations, clusters
• Tools: Matplotlib, Seaborn

Model Evaluation

• Metrics: Confusion matrix, precision, recall, F1-score, accuracy
• Model Comparison: Compare performance of different models using predefined metrics

Conclusion

• Summary: Detailed exploration of machine learning concepts, supervised and unsupervised learning, practical implementation in Google CoLab
• Community Learning: Encourages feedback and improvements from viewers