Computer Science

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor. This course introduces the main concepts of Python and its use in economic and econometric analyses. In particular, the course focuses on: 1) data types: definitions and use; 2) pandas; 3) basic programming structures (loops, if,...); 4) a primer on classes; and 5) applications to economics and econometrics.

This course discusses machine learning and its uses in business decision-making. Topics include: data extraction and exploration; basic models for classification and regression; training, hyper-parameter tuning, model evaluation, pre-processing; feature selection and generation; advanced models for classification and regression; unsupervised learning.

This is an advanced-level Data Science course, focusing on deep learning, which has witnessed great success over the past decade. Two of the most successful fields of deep learning are image processing and natural language processing. 

Some of the most successful applications of deep learning in image processing include object detection, image segmentation, and image classification. In natural language processing, deep learning has been used to develop applications such as machine translation, text classification, automatic summarization and question answering.  

The course begins with an overview of deep learning, and a review class for Python and the PyTorch library respectively. Then, the course studies linear algebra and calculus from numerical perspectives. The course also reviews the basics of statistics and information theory for deep learning and the basics of machine learning, including topics like overfitting, supervised and unsupervised learning, and stochastic gradient descent.  

The course introduces neural network models using the familiar linear and softmax regression, as well as the concept of multilayer perceptrons and the essential technique of backward propagation.  The course also studies various ways to regularize deep neural networks, such as putting norm penalties or allowing dropout, and how to do optimization for training these regularized deep neural networks. The latter half of the course focuses on convolutional neural networks for image processing and recurrent and recursive neural networks for natural language processing. Last, the recent important topic of fine-tuning a pre-trained large language model will also be covered. 

This course focuses on the practical aspects of the automated processing of human languages. It develops knowledge of useful and logical aspects, as well as useful prototypes of the same nature. The course introduces the basics of the programming language Python.

This course introduces students to modern programming techniques using the Java programming language as an example. The use of object-oriented concepts enables students to quickly work on complex tasks independently. In the practical exercises, students also learn how to use a development environment and a version management system (git) while programming. The programming language used is Java. -Java basics: * Data types, variables, operators, static methods / functions - Object orientation: * Classes and objects * Polymorphism with interfaces * Generics * Implementation inheritance - Java Collections - Error handling - Input / Output - GUI if necessary.

This course gives an introduction to several subdomains of intelligent autonomous systems and robotics, and an orientation about fundamental methods and algorithms within these domains. Content covered includes three-layer architecture, Perception-Action Cycle, Robotic architectures, world models, Robot Perception, SLAM, reasoning under uncertainty, MAP-Slam, actuation, picking, placing, and reasoning and planning. 

This course examines advanced concepts including software validation and verification, the theory of testing, and advanced design patterns. The course has a strong focus on the theoretical underpinning of software design. In the labs the theory is applied with contemporary tools with concrete examples.

This course examines the basic concepts and modern software architectures on distributed and parallel computing. Topics include: computer network primitives, distributed transactions and two-phase commits, webservices, parallelism and scalability models, distributed consistency models, distributed fault-tolerance, actor and monads, Facebook photo cache, Amazon key-value stores, Google Map-reduce, Spark, and TensorFlow.

Do Google and Facebook understand us better than we do ourselves? Are we becoming lab rats every time we go online? Is the impartially designed algorithm for predicting the probability of recidivism truly fair for sentencing individuals? When big data analytics are routinely applied in our daily lives, the ability to audit the adopted algorithms becomes crucial. This course aims to build students’ big data literacy through three major areas of focus: (1) Defining what big data is; (2) Providing an overview of existing big data analytical techniques; and (3) Discussing opportunities and challenges of big data analytics in tackling social problems.