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Machine Learning I, 2016

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Autor: Ricardo Aler
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

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.

 

Course Contents

Course guide , 2016

Mandatory readings , 2016

Exercises and projects , 2016

Evaluation tests , 2016

Instructors , 2016

Syllabus , 2016

Lecture notes , 2016

Course introduction , 2016

Labs exercises , 2016

Related resources , 2016

Attribute selection and transformation , 2016

Large Scale Machine Learning: MapReduce / Hadoop , 2016

Ensemble Methods , 2016

Model evaluation , 2016

Introduction to Machine Learning , 2016

Large Scale Machine Learning: Spark , 2016

Attribute Selection with train / test , 2016

attribute_selection_reduced.ipynb , 2016

decisionTreesHyperparameters.ipynb , 2016

decisionTreesTrainTest.ipynb , 2016

Intro-scikit , 2016

IntroDecisionTrees , 2016

mispark-kmeansComplete.html , 2016

mispark-kmeansComplete.ipynb , 2016

Python Tutorial , 2016

Final exam , 2016

Archivos.zip , 2016

Second assignment, part II: explanation for part II , 2016

displayDigits.ipynb , 2016

First assignment: Python notebook for the assignment , 2016

First assignment: Python programming for feature extraction , 2016

programmingAssignment.ppt , 2016

Second assignment, part I: Notebook for the first part of the second assignment , 2016

Second assignment, part II: SEMEION dataset , 2016

Third assignment: Python notebook , 2016

Third assignment: programming with SPARK , 2016

displayDigits.html , 2016

First assignment: Python notebook for the assignment , 2016

Second assignment, part I: Notebook for the first part of the second assignment , 2016

Third assignment: Python notebook , 2016

attribute_selection_reduced.html , 2016

decisionTreesHyperparameters.html , 2016

decisionTreesTrainTest.html , 2016

IntroDecisionTrees , 2016

mispark-kmeansComplete (1).html , 2016

Self-assessment for evaluation methods - Questions , 2016

Self-assessment for evaluation methods - Solutions , 2016

Self-assessment for K-nearest neighbour - Questions , 2016

Self-assessment for K-nearest neighbour - Solutions , 2016

Self-assessment for attribute selection methods - Questions , 2016

Self-assessment for attribute selection methods - Solutions , 2016

Self-assessment for decision trees - Questions , 2016

Self-assessment for decision trees - Solutions , 2016

RAler , 2016

banner_dpto_inf , 2016

Machine Learning , 2016

guide.pdf , 2016

arbolesdecisionreglasv2.pdf , 2016

Basic Models: Nearest Neighbours and Tree-Based Models , 2016

Guide to Models: KNN and decision trees , 2016

Guide to Model evaluation , 2016

Guide to Large Scale Machine Learning: MapReduce and Spark , 2016

Guide to Attribute selection and transformation , 2016

Guide to Ensembles , 2016

Guide to Introduction to Machine Learning , 2016

Download this Course , 2016

Third assignment: a guide to the solution , 2016

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