CS229 Machine Learning - Introduction Lecture

Lecture Overview

• Instructor: Andrew Ng
• Course Goal: Equip students with machine learning tools to build significant applications in various fields (tech, healthcare, transportation, etc.)
• Number of Students: ~800, class recordings available via SCPD
• Logistics: Intro to TAs and course structure (lectures, discussion sections, office hours)
• Honor Code: Encouraged to form study groups but complete individual homework

Course Introduction and Relevance

• Machine learning (ML) skills are in high demand
• ML is transforming industries similarly to how electricity did 100 years ago
• Examples of success with ML: Google Brain, Baidu AI
• Broad opportunities in various fields: tech, healthcare, law, logistics, etc.

Course Logistics

• Instructor: Andrew Ng
• TAs: PhD students with expertise spanning multiple domains (computer vision, NLP, etc.)
• Course Structure: Lectures on Mon and Wed, discussion sections on Fri
• Attendance: Optional for discussion sections, all content recorded
• Syllabus: Posted on the course website cs229.stanford.edu
• Tools: Piazza for discussions, Gradescope for grading, shift from MATLAB to Python for assignments
• Prerequisites: Basic programming, probability, and linear algebra

Honor Code and Homework

• Encourage study groups for discussion
• Homework must be completed individually
• Clear honor code guidelines on the course website
• CS229 recognized by employers for job interviews

Course Projects

• Importance: Real-world applications, small group projects (up to 3 people)
• Past projects include diagnosing cancer, creating art, etc.
• Projects can be viewed on the course website for inspiration

Main Topics Covered in CS229

1. Supervised Learning

• Definitions: Mapping from input X to label Y
  • Examples: Housing price prediction (regression), tumor classification (classification)
  • Algorithms: Linear regression, logistic regression, support vector machines (SVMs)
  • Applications: Healthcare, autonomous driving

2. Machine Learning Strategy (Learning Theory)

• Objective: Equip students with decision-making tools for ML projects
  • Emphasis on avoiding trial and error, employing systematic approaches
  • Introduction to Andrew Ng’s book on systematic engineering principles

3. Deep Learning (Neural Networks)

• Exploration of advanced tools in ML, especially deep learning
  • Covers basics and more advanced topics in neural networks

4. Unsupervised Learning

• Definitions: Finding structure in unlabeled data
  • Examples: Clustering, such as K-means, ICA (Independent Component Analysis)
  • Applications: Customer segmentation, social network analysis, genetic data analysis

5. Reinforcement Learning

• Definitions: Learning through reward and punishment
  • Examples: Autonomous helicopter flying, robot control, game playing (e.g., AlphaGo)
  • Applications: Robotics, logistics

Final Note

• Encouragement to start forming study groups and project teams
• Piazza for questions and class interactions
• Look forward to further classes on Wednesdays and full engagement in projects and learning.