Data Science and Machine Learning
Objectives
None
General characterization
Code
400088
Credits
7.5
Responsible teacher
Roberto André Pereira Henriques
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
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
CUC5. Supervised Learning Models
- Bayesian learning systems
- learning and classification.
- Regression and classification trees
- Neural networks
- Ensemble classifiers
Bibliography
- 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
Teaching method
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).
Evaluation method
English
Subject matter
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.