Base Knowledge
Some knowledge of the following topics is recommended:
– Neural networks
– Classifiers
– Python
Teaching Methodologies
Theoretical classes
– Presentation and discussion of the topics
– Presentation and discussion of assignments and examples
– Invited talks
Practical classes
– Assignment with data from LXDatalab
Learning Results
1 – Understand the key stages of the life cycle of a data analysis process
2 – Understand the importance of following an appropriate methodology in a data analysis process
3 – Follow CRISP-DM method in a data analysis process with real data
4 – Apply pre-processing methods and data visualization methods to real data
5 – Select, adapt and apply machine learning techniques to create detection, classification or prediction models
6 – Evaluate the quality of models created, qualitatively and quantitatively
Program
1. Introduction
1.1 What is data analysis
1.2 Importance of data analysis for organizations
2. Methodologies to follow in a Data Analysis process
2.1 CRISP-DM methodology
2.2. Business understanding, Data understanding, Data Wrangling
2.3.Modeling, Analysis of results and Deployment
3. Model deployment
3.1. Decision making when deploying a ML model
3.2. Hardware and software platforms
3.3. ONNX ML server
4. Application of Data Analysis
4.1. Data pre-processing and preparation
4.2. Object classification and detection with CNN
4.3. Text classification
4.4. Analysis and prediction with time series
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Data Science (The MIT Press Essential Knowledge series), John D. Kelleher and Brendan Tierney, MIT Press 2018, ISBN-13: 978-0262535434.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, Sebastian Raschka and Vahid Mirjalili, Packt Publishing 2019; 3rd edition.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618
John Kelleher, Brian Mac Namee, and Aoife D’Arcy; Fundamentals of Machine Learning for Predictive Data Analytics; The MIT Press, 2015, ISBN: 9780262044691
Aurélien Géron; Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow; O’Reilly Media 2019; ISBN-13 : 978-1492032649
Ian Pointer; Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications 1st Ed.; O’Reilly Media 2019.
Other resources available online, namely python manuals, sk-learn, tensorflow, pytorch, keras and onnx.