Courses

Introduction

This page describes a list of course that you can watch online.

Learning From Data

 caltech_data_608x211

Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just “knowing.” (Edx, 2014).

Link: http://goo.gl/J5BIaF

Data Analysis

 data_B-02

The course was ended, but you can have access to the course videos from Youtube videos tanged by weeks.

Link: http://goo.gl/aIDmUS

Introduction to Data Science

datascience

Introduction to Data Science is a class at Columbia University in the Department of Statistics. The course was designed and taught by Dr. Rachel Schutt in the Fall of 2012 (DataScience, 2014).

 

Link: http://columbiadatascience.com/

Machine Learning Carnegie Mellon University

carnegie

“This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning” (MLCarnegie, 2011).

 

Link: http://www.cs.cmu.edu/~tom/10701_sp11/

 CS109 Data Science

datascience

“The course page describes the course as: “Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.” (DSHarvard, 2014).

 

Link: http://www.cs109.org/

References

Edx. http://edx.org. Available 2014.

DataScience. http://columbiadatascience.com/2013/09/16/introduction-to-data-science-version-2-0/. Available 20114.

MLCarnegie. http://www.cs.cmu.edu/~tom/10701_sp11/. Available 2014.

DSHarvard. http://www.cs109.org/. Available 2014.

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