## Tag Archives: Machine Learning

## CS281: Advanced Machine Learning

http://www.seas.harvard.edu/courses/cs281/

This is the most complete Machine Learning course material that I have ever seen.

## Stanford Algorithm Analyzes Sentence Sentiment, Advances Machine Learning

Stanford Algorithm Analyzes Sentence Sentiment, Advances Machine Learning – See more at: http://goo.gl/jrZu1f

## Deep Learning

Deep Learning is attracting the Machine Learning community. To learn the model, two courses material are selected. The first, **Deep Learning and Unsupervised Feature Learning ** give a close look at the model, and the second, Unsupervised Feature Learning and Deep Learning, is under construction, but the first lectures are posted.

## Maximum Likelihood Estimator

When we apply Machine Learning, we often need to estimate the parameters of our model. It’s typically done by the use use of* Maximum Likelihood Estimation* (*MLE*). To understand the subject we found a very helpful material. To get this, access this link and enjoy that.

## Nasa Machine Learning Challenge

This post is for you that like challenges. Harvard NASA Tournament Lab (NTL) and in partnership with the University of California San Diego and TopCoder published a contest.

**Link to contest challenge video description.**

**Description**

What happens when you mash-up Crowdsourcing, Open Innovation, human exploration, space exploration, and machine learning algorithms? You get this amazing, first of its kind, algorithmic challenge! Brought to you by the Harvard NASA Tournament Lab (NTL) and in partnership with the University of California San Diego and TopCoder … we’re excited to bring you the Collective Minds and Machine Learning Exploration Challenge.

## University of Waikato launches MOOC on data mining

The Waikato university launched the first New Zealand MOOC. The course has focus on Machine Learning and do an explanation on how to apply Weka for this. Here goes a summary about the course:

Everybody talks about Data Mining and Big Data nowadays. Weka is a powerful, yet easy to use tool for machine learning and data mining. This course introduces you to practical data mining.

You could access the course by https://weka.waikato.ac.nz/.

Schedule:

- Class 1 – Getting started with Weka
- Class 2 – Evaluation
- Mid-course assessment
- Class 3 – Simple classifiers
- Class 4 – More classifiers
- Class 5 – Putting it all together

## Machine Learning Video Library

Machine Learning Video Library

On Caltech page you can found a great library about different subject applied to Machine Learning. Take a lot and enjoy the library.

## A First Encounter with Machine Learning

Max Welling is offering a book ‘A first Encounter with Machine Learning‘. This is intended as an introductory course to the field.

## Learning from Data

The course Learning from Data will be offered one more time by Caltech University. The course begins in April 2. There is no more session. It is the last chance to learn Machine Learning with one of the best Machine Learning MOOC course.

## The statistics: Making Sense of Data

The statistics: Making Sense of Data is starting next week.

Summary of the course;

We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate.

This course will provide an intuitive introduction to applied statistical reasoning, introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields. In particular, the course will cover methods of data collection, constructing effective graphical and numerical displays to understand the data, how to estimate and describe the error in estimates of some important quantities, and the key ideas in how statistical tests can be used to separate significant differences from those that are only a reflection of the natural variability in data.

Enjoy the course.