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CS229 Machine Learning - Introductory Lecture
Jul 20, 2024
CS229 Machine Learning - Introductory Lecture
General Introduction
Instructor:
Andrew Ng
CS229 has a rich history at Stanford, producing many industry experts and entrepreneurs.
Objective: Equip students with machine learning skills to impact various industries (tech, healthcare, transportation, etc.).
Importance of Machine Learning
AI and ML are transforming numerous fields, similar to the impact of electricity 100 years ago.
High demand for ML skills across industries (tech, academia, manufacturing, logistics, healthcare).
Machine learning's growth is exponential; opportunities are vast.
Making a career in ML now is akin to working on the Internet 20 years ago.
Course Logistics
Large class (~800 students), lectures are recorded and available on SCPD.
Instructor:
Andrew Ng
Teaching Team:
Includes experienced PhD students specializing in various ML fields (computer vision, NLP, etc.).
The course constantly updates to keep pace with advances in ML.
Prerequisites:
Basic computer science (Big O, data structures), probability, linear algebra, some familiarity with Python.
Tools:
Class uses Piazza for discussions and Gradescope for grading. Homeworks and projects will be in Python.
Honor Code
Encouraged to form study groups but students must write up their solutions individually.
Refer to the course website for detailed honor code guidelines.
Course Structure
Optional discussion sections on Fridays covering prerequisite material initially, then advanced topics.
Transition from MATLAB/Octave to Python for assignments.
Take-home midterm instead of in-person.
Project Work
Significant component of the course involves a hands-on project.
Projects often include applying ML to various domains, like healthcare, art, engineering, etc.
Encouraged to work in groups (ideally 2-3 students); groups of 4 allowed for exceptional projects.
Project groups should brainstorm, exploring previous projects on the course website for inspiration.
Lecture Topics Overview
Supervised Learning:
Learn to map inputs to outputs using labeled training data.
Examples: Housing prices (regression), tumor classification (classification).
Machine Learning Strategy:
Systematic approach to applying ML efficiently.
Deep Learning:
Intro to neural networks and training; specialized deep learning course available (CS230).
Unsupervised Learning:
Discover patterns in unlabeled data (clustering).
Reinforcement Learning:
Learn optimal behaviors through rewards (e.g., training a robot).
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Full transcript