Machine Learning I, 2016
Acciones de Documento
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RICARDO ALER MUR Computer Science Department 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
Attribute selection and transformation , 2016
Large Scale Machine Learning: MapReduce / Hadoop , 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
mispark-kmeansComplete.html , 2016
mispark-kmeansComplete.ipynb , 2016
Second assignment, part II: explanation for part II , 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
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
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
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
