College: Columbia University
Certificate Price: $249
Length: 12 Weeks
Instructors: John W. Paisley

Machine Learning

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major perspectives covered include:

probabilistic versus non-probabilistic modeling
supervised versus unsupervised learning
Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.

In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Similar Courses

Computing for Data Analysis

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data. The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program,…

Computer Hardware and Operating Systems

This is a self-paced course that provides an Introduction to Computer Hardware and Operating Systems This course will cover topics including: Fundamentals of system hardware Introduction to OS concepts OS…
C Programming: Advanced Data Types

C Programming: Advanced Data Types

In this course, part of the C Programming with Linux Professional Certificate program, you will define your own data types in C, and use the newly created types to more…
Saving Schools

Saving Schools

This course seeks to answer the question: how did a school system, once the envy of the world, stumble so that the performance in math, science, and reading of U.S.…
The Quantum World

The Quantum World

Welcome to The Quantum World! This course is an introduction to quantum chemistry: the application of quantum theory to atoms, molecules, and materials. You’ll learn about wavefunctions, probability, special notations,…
China and Communism

China and Communism

How did the Communists conquer China? What role does culture play? What are the successes and failures of the Chinese Communist Party after seizing power in 1949? What constitutes liberation?…
Big Data and Education

Big Data and Education

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and…
Lending

Lending, Crowdfunding, and Modern Investing

In this course, you’ll learn the foundational theories behind robo-advising, crowdfunding, and marketplace lending, and how to apply these theories to optimize your investments. Professor David Musto of the Wharton…