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Machine Learning

CS229


Description

Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.

This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory and adaptive control. Explore recent applications of machine learning and design and develop algorithms for machines.

Instructors

Andrew Ng, Associate Professor, Computer Science

Topics Include

  • Basics concepts of machine learning
  • Generative learning algorithms
  • Evaluating and debugging learning algorithms
  • Learning theory
  • Clustering, dimensionality reduction, and kernel methods
  • Reinforcement learning and control

Grading

  • Assignments (4)
  • Term project

Resources



Units

3.0 - 4.0

Students enrolling under the non degree option are required to take the course for 4.0 units.

Prerequisites

Linear algebra, basic probability and statistics.

Course Preview

We highly recommend watching the course preview to ensure you have the requisite background and understand the scope of material covered.

Tuition & Fees

For course tuition, reduced tuition (SCPD member companies and United States Armed forces), and fees, please click Tuition & Fees.

Please complete a Course Inquiry so that we may notify you when enrollment opens.