Machine Learning I, 2016

The main goals of this course are: to introduce the basic concepts of Machine Learning and Big Data Machine Learning; to describe the main areas, techniques, and processes in Machine Learning; to introduce some of the main tools in (Big Data) Machine Learning

Course Image

RICARDO ALER MUR

Computer Science Department
Universidad Carlos III de Madrid

Machine Learning

Master in Big Data Analytics

 

January 2017

By Tej3478 (Own work) [CC BY-SA 4.0], via Wikimedia Commons

Course
Introduction

Theorethical and Lab hours: 10.5 (theoretical) 10.5 (lab hours)
Total hours: 21 hours

 

PRERREQUISITES AND RECOMMENDED PREVIOUS KNOWLEDGE

Programming.

 

GENERAL DESCRIPTION OF THE SUBJECT

The main goals of this course are:

- To introduce the basic concepts of Machine Learning and Big Data Machine Learning
- To describe the main areas, techniques, and processes in Machine Learning
- To introduce some of the main tools in (Big Data) Machine Learning

 

OBJETIVES: KNOWLEDGE AND SKILLS

Specific skills:

To identify and select software tools suitable for the treatment of large amounts of data

To design systems for processing data, from the collection and initial filtering, statistical analysis, and the submission of final results

To use techniques and operation research tools in procedures with massive data for analysing or displaying results in decision support systems

To apply the basic and fundamental principles of machine learning to design procedures and improving them

To identify the opportunity to use machine learning to solve real problems

To perform detailed analysis and design of applications based on machine learning

Learning outcomes:

- Basic and fundamental knowledge of machine learning
- Understanding of basic machine learning techniques
- Practical application of basic machine learning techniques in real problems
- Capacity for analyzing the most appropriate tasks for each technique
- To understand when to use machine learning techniques for solving real problems

 

TEACHING MATERIAL

Slides and tutorials used during the lectures are provided.

 

ASSESSMENT ACTIVITIES OR PRACTICAL ASSIGNMENTS

Several labs for learning Python, Scikit-learn, and pySpark are provided as Python notebooks.

The course features three assignments: one for assessing basic Python programming, one for assessing basic Machine Learning concepts with Scikit-learn, and a final one for Spark.

 

Citation: Aler, R., Aler, R. (2016, April 21). Machine Learning I. Retrieved November 23, 2017, from OCW - UC3M Web site: http://ocw.uc3m.es/ingenieria-informatica/machine-learning-i.
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