• Anytime
    Application Deadline
  • 84 days
    Duration
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Overview

Machine Learning is the basis for the most exciting careers in data analysis today. In this Machine Learning course, part of the Artificial Intelligence MicroMasters program from Columbia University - ColumbiaX you will 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

What you'll learn

  • Supervised learning techniques for regression and classification
  • Unsupervised learning techniques for data modeling and analysis
  • Probabilistic versus non-probabilistic viewpoints
  • Optimization and inference algorithms for model 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.

Detailed Programme Facts

  • Deadline and start date A student can apply at any time for this programme, there is no deadline.
  • Programme intensity Part-time
    • Average part-time duration 84 days
    • Intensity 8 hrs/week
    • Part-time variant
      Flexible
    • Duration description

      12 weeks; 8–10 hours

  • Languages
    • English
  • Delivery mode
    Online
  • More information Go to the programme website

Programme Structure

Courses include:

  • Linear and logistic regression
  • Support vector machines
  • Tree classifiers
  • Boosting

English Language Requirements

This programme may require students to demonstrate proficiency in English.

General Requirements

  • Calculus
  • Linear algebra
  • Probability and statistical concepts
  • Coding and comfort with data manipulation

Tuition Fee

Add a Verified Certificate for $375 USD

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Funding

Check the programme website for information about funding options.

Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

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