Machine Learning


Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.

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 modelling
  • Supervised versus unsupervised learning

Topics include: classification and regression, clustering methods, sequential models, matrix factorisation, topic modelling 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 optimisation. 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 factorisation, and sequential models for order-dependent data.

Some applications of these models include object recommendation and topic modelling. This course is part of the Artificial Intelligence MicroMasters® Programme, offered through the edX® platform.

Associated Courses:
Artificial Intelligence (AI)
Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
View the Artificial Intelligence (AI) course

Learn the core techniques for representing robots that perform physical tasks in the real world.
View the Robotics course

Animation and CGI Motion
Learn the science behind movie animation from the Director of Columbia’s Computer Graphics Group.
View the Animation and CGI Motion course

Associated Programmes:
MicroMasters® Programme: Artificial Intelligence


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

edX® and MicroMasters® are registered trademarks of edX® Inc. All Rights Reserved.

  • Programme duration
    12 weeks
  • Estimated effort
    8-10 hours per week
  • Fee
  • Institution
  • Language
Start Dates
  • Supervised learning techniques for regression and classification
  • Unsupervised learning techniques for data modelling and analysis
  • Probabilistic versus non-probabilistic viewpoints
  • Optimisation and inference algorithms for model learning

Course Syllabus

  • Week 1: Maximum likelihood estimation, linear regression, least squares
  • Week 2: Ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
  • Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
  • Week 4: Nearest neighbour classification, Bayes classifiers, linear classifiers, perceptron
  • Week 5: Logistic regression, Laplace approximation, kernel methods, Gaussian processes
  • Week 6: Maximum margin, support vector machines, trees, random forests, boosting
  • Week 7: Clustering, k-means, EM algorithm, missing data
  • Week 8: Mixtures of Gaussians, matrix factorisation
  • Week 9: Non-negative matrix factorisation, latent factor models, PCA and variations
  • Week 10: Markov models, hidden Markov models
  • Week 11: Continuous state-space models, association analysis
  • Week 12: Model selection, next steps

Arlene Lanser

Pearson Student Advisor