CSU 29 Machine Learning Lecture Notes

Introduction to CSU 29

• Taught by Andrew Ng, previously taught at Stanford.
• Class designed to help students become experts in machine learning.
• The potential to impact various industries such as tech, healthcare, transportation, etc.

The Importance of Machine Learning

• AI is compared to electricity in its potential to transform industries.
• Growing demand for AI and machine learning skills across various sectors.
• Machine learning's rapid advancement presents numerous opportunities for application.

Class Logistics

• Large class size, but lectures and discussions are recorded and available online.
• Andrew Ng introduced the teaching team, including TAs with expertise in different areas of machine learning.
• Goal: Equip students to apply machine learning in both academic and industry settings.
• Encouragement to form study groups and collaborate on projects.

Course Structure and Expectations

• Prerequisites: Basic computer science principles, probability, statistics, linear algebra.
  • Review sessions available for those needing a refresher.
• Transition from MATLAB to Python for assignments.
• Class projects are a major component; encourage forming groups of 2-3.
• Honor code: Discuss but write homework independently.

Course Content Overview

• Supervised Learning: Most used ML method.
  • Regression and classification problems.
  • Example: Housing prices (regression) and tumor classification (classification).
• Machine Learning Strategy: Focus on systematic problem-solving and application.
  • Importance of efficient application and decision-making in ML projects.
• Deep Learning: Introduction and basic training of neural networks.
• Unsupervised Learning: Finding patterns in data without labels.
  • Tools like clustering algorithms (e.g., k-means) and applications in various fields.
• Reinforcement Learning: Algorithm learning from trial and error (reward/punishment system).
  • Applications in robotics, game-playing, and optimization.

Additional Resources

• Encouragement to use Piazza for class discussions and questions.
• Course will also use Gradescope for grading.
• Take-home midterm instead of a timed midterm.

Advice and Encouragement

• Encouraged to explore multiple classes and gain diverse perspectives in AI and ML.
• Machine learning offers both economic opportunities and the potential to make meaningful societal contributions.