Machine Learning I
Learn the principles of supervised and unsupervised machine learning techniques.
Modules/Weeks
Weekly Effort
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School
Format
Cost
Course Description
- Approach supervised learning problems, including linear regression and classification.
- Tackle unsupervised learning problems, such as clustering and dimensionality reduction.
- Understand the mathematical principles behind machine learning techniques.
- Apply machine learning concepts to real-world problems through coding projects.
This course is available at no cost and includes full access to all instructional materials, videos, and assessments. Learners who successfully complete all course requirements will have the option to purchase a verified certificate of completion for $20.
Course Prerequisites
- Strong foundation in calculus
- Proficiency in linear algebra
- Grasp of probability and statistical concepts.
- Coding skills and comfort with data manipulation
What You Will Learn
By the end of this course, learners will be able to:
Approach supervised learning problems, including linear regression and classification.
Tackle unsupervised learning problems, such as clustering and dimensionality reduction.
Understand the mathematical principles behind machine learning techniques.
Apply machine learning concepts to real-world problems through coding projects.
Course Outline
Module 1: Maximum likelihood
Module 2: Regression 1
Module 3: Regression 2
Module 4: Classification 1
Module 5: Classification 2
Module 6: Extended classification
Instructors
John Paisley joined the Department of Electrical Engineering at Columbia University in Fall 2013 and is an affiliated faculty member of the Data Science Institute at Columbia University. He received the B.S., M.S. and Ph.D. degrees in Electrical and Computer Engineering from Duke University in 2004, 2007 and 2010. He was then a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley, where he worked on developing probabilistic models for large-scale text and image processing applications. He is particularly interested in developing Bayesian models and posterior inference techniques that address the Big Data problem, with applications to data analysis and exploration, recommendation systems, information retrieval, and compressed sensing.
Please note that there are no instructors or course assistants actively monitoring this course.