Advanced Training Program in
Data Science & Machine Learning
Combining solid foundational knowledge with the practical application of state-of-the-art techniques.
The Advanced Training Program in Data Science and Machine Learning aims to qualify both well-established professionals and graduates from related fields in data science and machine learning and, more broadly, in the constantly evolving field of artificial intelligence.
The program offers its students the opportunity to learn from some of the most renowned researchers in the world, who are developing cutting-edge technologies that are becoming ever more present in widely deployed systems.
WORLD CLASS FACULTY
João Magalhães
FCT-UNL
Mário Figueiredo
Técnico
Sara C. Madeira
FCUL
INDUSTRY & ACADEMIA PARTNERS
WHAT WE OFFER
Build your own path
Core Module
Foundations of Data Science
Date TBA
- This course will introduce the foundations of data science. Participants will understand how different, real-world, types of data are represented and processed by the mathematical tools of linear algebra and probability. An introduction to key data science programming tools and environments will be also covered in this module. A bird-eyes view of the data science pipeline will then outline the different courses offered by the CMU Portugal Data Science Executive Program.
Coordinator(s): CMU faculty to be announced
ECTS: 2
Lecture hours/ weeks: 30h/1w
Fee: TBA
Core Module
Data Collection and Preprocessing
Date TBA
This course will cover the ways in which data can be obtained and prepared for further analysis. It will consider different data sources, types, and collection methods, and expose the main challenges encountered with datasets (e.g., incomplete, noisy, inconsistent, biased). The course will have a focus on data quality and how to make data tidy, particularly exposing the concepts of data cleaning, integration, transformation, reduction, and discretization. Overall, the course will provide the basic knowledge required for collecting, cleaning, integrating, exploring and sharing data.
Coordinator(s): Tiago Guerreiro (FCUL) / Cátia Pesquita (FCUL)
ECTS: 1
Lecture hours/ weeks: 30h/1w
Fee: TBA
Core Module
Machine Learning
Date TBA
This course will cover the fundamental concepts and tools of machine learning. The main large families of learning problems will be described (namely, supervised, unsupervised, and reinforcement learning), as well as the most important classes of methods used to address these problems. The course will provide a comprehensive view of modern machine learning, putting all the learning scenarios and techniques in perspective, pointing out their assumptions, capabilities, and potentials weaknesses.
Coordinator(s): Mário Figueiredo (Técnico)
ECTS: 1
Lecture hours/ weeks: 30h/1w
Fee: TBA
Search and Recommender Systems
Date TBA
This course covers the key techniques needed to build and deploy large-scale search and recommender systems over unstructured data (text and image). The key concepts studied in this module are information representation, indexing, querying, searching, ranking recommending by relevance. This achieved through the theoretical presentation of the methods and experimental analysis of the methods.
Coordinator(s): CMU faculty to be announced
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
Information Visualization
Date TBA
This course will provide an introduction to information visualization and an overview of the most common techniques. Participants will learn the different dataset types in information visualization and the visualization techniques more appropriate for each of them. In particular tabular (multivariate and multidimensional), spatial, geospatial, time-oriented, trees, graphs and text datasets. Interaction techniques in visualization (overview, zooming, filtering, details-on-demand) and visual perception topics will also be covered in this module. Finally, participants will learn how to evaluate the usability and user experience of visualization techniques.
Coordinator(s): Beatriz Carmo (FCUL) / Manuel João Fonseca (FCUL)
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
Deep Learning
Date TBA
This course is an introduction to the basic methods of deep learning, starting with the perceptron, deep multi-layer neural architectures and the chain-rule. More recent deep learning architectures for image recognition, machine translations, etc., will be studied in hands-on laboratories with TensorFlow. Part of this course will also look into the aspect of feature engineering vs feature learning, and discuss black-box models, nowadays a critical issue with GDPR.
Coordinator(s): Mário Figueiredo (Técnico)
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
Deep Learning for Image and Language
Date TBA
Understanding image and language data is a critical task in many information intensive domains. Deep Learning has fueled many advances in image and language understanding. This course will cover the methods and techniques behind critical advances in Word embeddings, Multimodal embeddings, Machine Translation, Image Captioning and Visual Question Answering.
Coordinator(s): João Magalhães (FCT-UNL)
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
Cloud based Data Processing
Date TBA
This course provides an overview of the parallel and distributed systems that form the infrastructure upon which machine learning algorithms work, and the tools and techniques for running those systems on cloud computing platforms. Topics include programming models for ML, parallel and distributed processing, model serving, cloud computing systems, distributed machine learning platforms, GPUs and other accelerators.
Coordinator(s): Nuno Preguiça (FCT-UNL), Rodrigo Rodrigues (Técnico)
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
Complex Data Analysis
Date TBA
This course covers core approaches for processing, mining, and learning complex data. The key concepts, algorithms and challenges in complex data analysis are studied, together with the main approaches to analyse data with a complex structure (temporal and spatio temporal data, graphs and networks, etc). By the end of the course, students should be able to identify and understand typical scenarios involving complex data analysis and propose solutions for real applications aiming at discovering patterns and learning models from heterogeneous data with a complex structure.
Coordinator(s): Sara Madeira (FCUL)
ECTS: 1
Lecture hours/ weeks: 18h/1w
Fee: TBA
WHO CAN APPLY
Candidates with a master’s degree or a pre-Bologna undergraduate degree in an area that is related to Computer Science and Artificial Intelligence, including but not limited to Electrical Engineering, Mechanical Engineering, or Mathematics.
Preference is given to candidates with prior professional experience in the field of Information and Communication Technologies (ICT), but this experience is not a strict requirement to enter the program.
WHAT STUDENTS CAN EXPECT
