Data Science and Machine Learning

Caracterização geral

Pré-requisitos

course unit content

 

CUC1. Introduction to Data Science

CUC2. Data Science methodology and Introduction to Machine Learning

CUC3. Working with Data: data pre-processing and Data visualization

CUC4. Unsupervised Learning Models

• Introduction to cluster analysis
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CUC5. Supervised Learning Models

• Bayesian learning systems
• learning and classification.
• Regression and classification trees
• Neural networks
• Ensemble classifiers

Bibliografia

• Data Mining and Predictive Analytics, Daniel T. Larose, Chantal D. Larose, Wiley, 2015
• Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, Dean Abbott Wiley, 2014
• Machine Learning, Tom M. Mitchell, McGraw Hill, 1997
• Pattern Recognition and Machine Learning, Christopher M. Bishop
• Machine Learning Yearning, By Andrew Ng

Método de ensino

1st term exam

• Exam (80%)
• Practical handouts (20%)

 

2nd term exam

• Exam (80%)
• Practical handouts (20%)

Minimum grade of 9.0 (in 20) for the exams

The delivery dates for the handout and project in the program below.

Handouts not delivered until the deadline will be penalized (up to 3 values).

Método de avaliação

English

Conteúdo

The course is based on a mix of theoretical lectures and practical lectures and tutorials. The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples.

The main objective of the practical classes is to familiarize students with the software to perform the analysis and data explorations tasks.

Cursos

Cursos onde a unidade curricular é leccionada: