Course History: Long-standing course at Stanford, pivotal in fostering generations of machine learning experts.
Goals: Provide tools to become future industry leaders, empower students to build impactful ML applications in diverse sectors such as healthcare, transportation, and tech startups.
Demand for ML skills: Enormous demand in both academia and industry. New opportunities constantly emerging.
Logistics
Class Size: Room capacity exceeded; 800 students enrolled.
Course Recordings: Available online via SCPD the same day as lectures.
Teaching Team:
Class coordinator and several TAs with expertise in various ML fields (e.g., computer vision, NLP, robotics).
TAs provide mentoring for course projects, offering domain-specific advice.
Prerequisites
Basic Computer Science: Big O notation, data structures (queues, stacks, binary trees).
Probability: Random variables, expected values, variance.
Linear Algebra: Matrices, vectors, matrix operations, eigenvectors.
Programming: Python with NumPy (transitioning from MATLAB/Octave).
Honor Code
Collaboration: Encouraged to form study groups but must write homework independently. Solutions should be your own work.
Integrity: Essential for maintaining the course's reputation and its value to employers.
Course Components
Lectures: Mondays and Wednesdays, covering core ML topics.
Discussion Sections: Fridays, optional attendance. Cover prerequisites in the initial weeks and advanced topics later.
Project: A significant component involving small group work. Find a project group (1-3 people, occasionally 4 for larger projects).
Digital Tools: Piazza for online discussions and Gradescope for grading.
Midterm: Transitioning to a take-home midterm instead of an in-class exam.
Machine Learning (ML) Overview
Definitions
Arthur Samuel: "Field of study that gives computers the ability to learn without being explicitly programmed."
Tom Mitchell: "A program is said to learn from experience E with respect to task T and some performance measure P..."
Types of Learning
Supervised Learning
Task: Learn a mapping from inputs (X) to outputs (Y) using labeled data.
Clustering: Group similar data points (e.g., market segmentation, social network analysis).
Dimensionality Reduction: Reduce number of random variables (e.g., PCA).
Applications: Genetic data analysis, market segmentation, social network analysis.
Reinforcement Learning
Task: Learn optimal actions through trial and error to maximize cumulative reward.
Applications: Robotics (e.g., helicopter flying, autonomous robots), game playing (e.g., AlphaGo).
Deep Learning
Focus: Training deep neural networks for complex tasks.
Applications: Image recognition, NLP, and other advanced ML tasks.
Course: CS230 focuses solely on deep learning if interested.
Machine Learning Strategy
Goal: Make ML a systematic engineering discipline. Help you efficiently build effective ML systems by making informed decisions (e.g., data collection, algorithm choice).
Approach: Systematic strategies over experience-based decisions.
Tools: Learning theory, error analysis, and performance metrics.
Course Updates
Programming language: Shift from MATLAB/Octave to Python/NumPy.
Midterm format: Transitioning to a take-home midterm.
Final Notes
Encouragement: Form study and project groups early, engage on Piazza for discussions, and start brainstorming project ideas.
Office Hours: Increased to 60 hours per week to provide ample support.