Machine Learning I
Course introduction
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Machine Learning I
RICARDO ALER MUR |
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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 COURSE
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
OBJECTIVES: 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.
PRACTICAL ASSIGMENTS AND ASSESSMENT ACTIVITIES
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.