CS229 Machine Learning Lecture Notes

Jun 26, 2024

CS229 Machine Learning - Lecture Notes

Introduction

  • Course Instructor: Andrew Ng
  • Course Legacy: Taught at Stanford for many years.
  • Impact: Aims to make students experts in machine learning, influencing their future in tech companies, startups, or other industries like healthcare and transportation.
  • Importance of Machine Learning: Compared to the significance of electricity 100 years ago.

Logistics & Initial Information

  • Course Enrollment: Room seats 300+, but 800 students enrolled. Classes recorded and broadcast via SCPD.
  • Instructor: Andrew Ng and a team of TAs with various expertise in machine learning fields.
  • Prerequisites: Basic knowledge of computer science (Big O, queues, stacks, etc.), probability (random variables, expectation, variance), and linear algebra (matrices, vectors, eigenvectors).
  • Programming: Transitioning from MATLAB/Octave to Python/NumPy.
  • Honor Code: Collaboration allowed but final work must be individual. Detailed honor code on the course website.
  • Discussion Platform: Piazza for class discussions, questions, and technical issues.
  • Grading: Using Gradescope for online grading.
  • Midterm: Take-home midterm instead of in-class.

Course Structure and Content

Supervised Learning

  • Definition: Learn a function mapping from inputs (X) to outputs (Y) using labeled data.
  • **Types: **
    • Regression: Predict continuous values (e.g., housing prices based on size).
    • Classification: Predict discrete values (e.g., tumor classification as benign or malignant).
  • Techniques:
    • Fitting models (linear, quadratic, etc.)
    • Logistic Regression
    • Support Vector Machines (SVM) with infinite-dimensional vectors.

Machine Learning Strategy (aka Learning Theory)

  • Importance of Strategy: Efficiently implementing learning algorithms and making systematic decisions.
  • Debugging Algorithms: Deploying systematic engineering principles rather than black magic or tribal knowledge.
  • Example: Distinguishing between optimizing code methodically vs. blindly.

Deep Learning

  • Subset of Machine Learning: Focuses on neural networks, currently a highly advancing field.
  • **Course Comparison: **
    • CS229: Broader machine learning algorithms
    • CS230: Focused exclusively on deep learning.

Unsupervised Learning

  • Definition: Identify structure in unlabeled data (only X).
  • Examples:
    • Clustering: Discovering groups in data (K-means clustering).
    • Applications: Genetic data analysis, organizing social networks, market segmentation, astronomical data clustering.

Reinforcement Learning

  • Definition: Learning through rewards and penalties (training a dog analogy).
  • Applications: Autonomous helicopters, robotic dogs, game playing (e.g., Atari games, AlphaGo).

Miscellaneous

  • Class Projects: Group projects involving application of machine learning techniques. Small groups (size 2-3) encouraged; higher standards for groups of 4.
  • Office Hours: 60 hours/week available for student support.

Next Steps

  • Form Study Groups: Collaborate for better learning experiences.
  • Explore Project Ideas: Begin brainstorming project topics for course project.

Note: All materials, lectures, and resources available on the course website including project inspirations from previous years.