# 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.

What you'll learn

- Calculus
- Linear algebra
- Probability and statistical concepts
- Coding and comfort with data manipulation
- probabilistic versus non-probabilistic modeling
- supervised versus unsupervised learning
- 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

## Get a Reminder

Rating | 5.0★ based on 4 ratings |
---|---|

Length | 12 weeks |

Effort | 8 - 10 hours per week |

Starts | On Demand (Start anytime) |

Cost | $249 |

From | Columbia University, ColumbiaX via edX |

Instructors | Professor John W. Paisley, John W. Paisley |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming |

Tags | Computer Science |

## Get a Reminder

## Similar Courses

## What people are saying

**
my effectiveness has improved
**

My effectiveness has improved, even using models I thought I was very familiar with.

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other similar courses commented
**

Many of the students who had done other similar courses commented it was at a much higher level than most.

**
problem never happened again
**

Just have to wait that your files are saved before submitting them to grading (my first submission was empty because of that, the problem never happened again when I cared about it).

**
techniques except neural networks
**

The topics covered are numerous, too many to list without putting you to sleep, but they span all of the common machine learning techniques except neural networks, which is a subject in itself (and is nicely covered at a high level in the AI course).

**
things like categorical variables
**

There was no info on things like categorical variables, dummies or dealing with outliers for example.

**
background were pretty rusted
**

I would like to thank him warmly, because my linear algebra and math background were pretty rusted, but it was a so-interesting 3 months, challenging for some parts but at the end of the day, it open perspectives, it provides extra tools of knowledge, it gives you keys of understanding.

**
communicating difficult concepts well
**

The lecturer was excellent and did a good job of communicating difficult concepts well.

**
either need prior ml
**

For that context, you either need prior ML training or to have taken the first course in the AI MicroMaster series, Artificial Intelligence with Ansaf Salleb-Aouissi, which treats most of the topics in this course at a higher, more introductory level.

**
1.25x speed delivered
**

Prof. Paisley's lectures are clear and deftly unpack difficult mathematical principles and derivations, although he speaks a bit slowly and I found that playing the lectures at 1.25x speed delivered a more natural cadence.

**
list without putting
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provides extra tools
**

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really provide insight
**

The materials are mathematically rigorous and really provide insight on how to analyse, design and evaluate learning algorithms.

## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Counseling Theories & Models Part-Time Faculty $17k

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Professor of Sequential Art $101k

Assistant Adjunct Professor Statistical Models $122k

Risk Analytics Tools and Models Program Manager $136k

## Write a review

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Rating | 5.0★ based on 4 ratings |
---|---|

Length | 12 weeks |

Effort | 8 - 10 hours per week |

Starts | On Demand (Start anytime) |

Cost | $249 |

From | Columbia University, ColumbiaX via edX |

Instructors | Professor John W. Paisley, John W. Paisley |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming |

Tags | Computer Science |

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