Lecture Notes: Introduction to Machine Learning with Kylie Ying

Introduction

• Kylie Ying has worked at MIT, CERN, and Free Code Camp.
• She is a physicist, engineer, and renowned for her expertise.
• The lecture is titled Machine Learning for Everyone.
• Focus on supervised and unsupervised learning models, their logic, math, and implementation using Google CoLab.
• Audience encouraged to engage and correct mistakes in the comments for communal learning.

Resources and Setup

• Data used: UCI machine learning repository.
• Example dataset: Magic Gamma Telescope.
• Description: Telescope captures high energy particles, and the dataset contains attributes like length, width, size, asymmetry, etc. to classify particles (gamma vs hadron).
• Tools and libraries: NumPy, Pandas, Matplotlib, Sklearn, TensorFlow.
• Instructions: Download dataset, import libraries, set up and run cells in Google CoLab.

Supervised Learning

Concepts

• Machine Learning (ML): Algorithms that enable computers to learn from data without explicit programming.
• Artificial Intelligence (AI): Simulating human behavior on machines.
• Data Science: Drawing insights from data, often involves ML.
• Types of ML:
  • Supervised Learning: Uses labeled inputs and outputs.
  • Unsupervised Learning: Finds hidden patterns in unlabeled data.
  • Reinforcement Learning: Learning via rewards and penalties.
• Features: Inputs to the model.
• Labels: Outputs to be predicted.
• Training, Validation, and Test Datasets: Split datasets to evaluate models.
• Loss Function: Determines how well the model is performing.

Models

1. K-Nearest Neighbors (KNN)
   • Classifies a point based on its k-nearest neighbors.
   • Uses Euclidean distance for calculation.
2. Naive Bayes
   • Based on Bayes Theorem.
   • Assumes independence among features.
3. Logistic Regression
   • Uses a sigmoid function for binary classification.
4. Support Vector Machines (SVM)
   • Finds the hyperplane that best separates classes.
   • Maximizes margins between different classes.
5. Neural Networks (NN)
   • Complex architectures with input, hidden, and output layers.
   • Uses activation functions (sigmoid, tanh, ReLU) to introduce non-linearity.
   • Training (Backpropagation): Adjust weights to minimize loss.

Implementation

• Set up and import necessary libraries.
• Use datasets from UCI repository, preprocess datasets (e.g., converting class labels to integers, scaling features).
• Build models (KNN, Naive Bayes, Logistic Regression, SVM, Neural Net) using Sklearn and TensorFlow.
• Evaluate models using metrics like accuracy, precision, recall, F1-score.
• Tune hyperparameters using techniques like Grid Search.

Unsupervised Learning

Concepts

• No labeled data involved.
• Clustering Algorithms: Group data points into clusters based on similarity.

Models

1. K-Means Clustering
   • Partition data into k clusters.
   • Iteratively recalculates cluster centroids and reassigns points.
   • Uses Expectation-Maximization (EM) algorithm.
2. Principal Component Analysis (PCA)
   • Dimensionality reduction technique.
   • Projects data onto principal components with maximum variance.
   • Useful for visualizing and simplifying data.

Implementation

• Use datasets from UCI repository (e.g., seeds dataset with geometric parameters of wheat kernels).
• Visualize data using Seaborn, Matplotlib.
• Apply K-Means Clustering to identify natural groupings in data.
• Use PCA to reduce dimensions and visualize high-dimensional data.
• Evaluate clustering results by comparing predicted clusters with actual classes.

Closing Remarks

• Summary: Walkthrough of supervised and unsupervised learning, their models, logic, and implementations in Google CoLab.
• Encouragement for community interaction and learning together.

References

• UCI Machine Learning Repository for diverse datasets.
• Google CoLab for practical implementations.
• Sklearn, TensorFlow, Pandas, NumPy for model building and data manipulation.