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Presentation
Presentation
Following Fundamentals of Statistics for Data Science and Applied Programming for Data Science courses, this course aims to introduce the concepts of supervised machine learning. The introduction of the concepts of regression and classification allows the student to identify the type of problem to be addressed. This allows students to acquire skills to analyze problems and define strategies to deal with it. The student will be able to identify possible approaches, among different possibilities of modeling the problem, and decide on the best solution for a given set of data.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
2 | Mandatory | Português
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Code
Code
ULP6613-24297
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
PC1. Data Preprocessing PC2. Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression PC3. Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification PC4. Clustering: K-Means, Hierarchical Clustering PC4. Dimensionality Reduction PC5. Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
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Objectives
Objectives
LG1. The ability of organization and planning, the ability of analysis and synthesis, the ability to solve problems and make decisions, the ability to work in a team, the ability to put in practice the theoretical knowledge acquired and the ability to develop new ideas. LG2. Regarding the technical component, at the end of the course, the student should be able to discuss the main topics and concepts, such as: Master Machine Learning on Python Have an overview of many Machine Learning models Make accurate predictions and powerful analysis Make robust Machine Learning models Create strong added value to your business using Machine Learning Introduce specific topics like Reinforcement Learning and Deep Learning Handle advanced techniques like Dimensionality Reduction Build several Machine Learning models and understand how to combine them to solve a problem.
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Teaching methodologies and assessment
Teaching methodologies and assessment
Implementation of learning techniques based on active methodologies.
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References
References
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O'Reilly Media. ISBN: 978-1491962299
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Office Hours
Office Hours
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Mobility
Mobility
No
