Base Knowledge
Some knowledge of the following topics is recommended:
– Classical and deep machine learning
– 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 – Know the professions and profiles in the area of Data Analysis
2 – Understand the key stages of the life cycle of a data analysis process
3 – Understand the importance of following an appropriate methodology in a data analysis process
4 – Follow CRISP-DM method in a data analysis process with real data
5 – Apply pre-processing methods and data visualization methods to real data
6 – Select, adapt and apply machine learning techniques to create detection, classification or prediction models
7 – 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
1.3. Professional profiles in Data Analysis
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
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing. Loc. 1A-4-187 (ISEC) – 18950.
Chollet, F. (2021). Deep learning with Python. Shelter Island, NY : Manning, cop. Loc. 1A-4-205 (ISEC) – 19262
Géron, A. (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media. ISBN-13 : 978-1492032649. Loc. 1A-4-179 (ISEC) – 18948.
Kelleher, J., & Tierney, B. (2018),.Data Science (The MIT Press Essential Knowledge series). MIT Press. ISBN-13: 978-0262535434.
Goodfellow, I., & Bengio, Y., & Couville, A. (2016). Deep learning. The MIT Press. ISBN: 0262035618.
Kelleher, J., & Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press. ISBN: 9780262044691.
Pointer, A. (2019). Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications (1st Ed.). O’Reilly Media.
Outros recursos disponíveis online, designadamente manuais do Python, sk-learn, tensorflow, pytorch, keras e onnx.