Computer Science

This course explores topics of machine learning and deep learning, examining both the foundations and applications of the topics. Starting with the basics of how to pre-process data, the course then ventures into linear models. Further topics include cross validation, support vector machines, kernels, regularization, boosting, bootstrap aggregating, and stacking.

This course introduces the basic principles and hardware structures of a modern programmable computer. Students will explore computer architecture as the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. 

 Students will learn how to design the control and datapath for a pipelined RISC processor and how to design fast memory and storage systems. The principles presented in lecture are reinforced in the laboratory through design and simulation of a register transfer (RT) implementation of a RISC processor pipeline in Verilog.   

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. At the end of the course the student knows and understands: - the motivation and the components of the Data Mining process; - the general concepts, technologies and methodologies of Data Warehouse, OLAP and Data Lake, as enabling factors of the Data Mining process; - the principles and the most relevant use cases of a wide set of Machine Learning algorithms which are used to extract relevant and actionable information from large amounts of data. At the end of the course the student is able to: - design the main steps of a Data Mining process - choose the Machine Learning methods best suited for the process - evaluate the quality of the result in order to support strategic and operational decisions. The course is divided into two parts: Data Mining and Machine Learning.

The first part of this course covers fundamental topics in discrete mathematics that underlie many areas of computer science and presents standard mathematical reasoning and proof techniques such as proof by induction. The second part of this course covers discrete and continuous probability theory, including standard definitions and commonly used distributions and their applications.

Through the teaching of this course, students are introduced to the basic ideas and methods of object-oriented programming. Through learning and combining computer practice, students can understand the general methods of computer problem solving, master interface production, program writing and debugging, and enable students to have the ability to write simple application programs. This will lay a solid foundation for further self-study of other programming languages and improve programming skills in the future, while also improving students' logical thinking ability and meticulous thinking ability.

The basic contents include: VB development environment and basic programming steps, basic controls and language basics, basic control structures, arrays, procedures, commonly used controls, menus, multiple forms and files, etc.

This course provides instruction in building cutting-edge interactive systems and guides to design futuristic experiences. Students gather in Make Reality Space in a studio format to construct software and hardware prototypes. Topics for each semester may change and evolve towards ultimate reality. 
 
This course focuses on mixed reality technologies. Specifically, students use Meta Quest 3, Intel Realsense cameras, computer vision toolkits, 3D printed props, and Unity game engines to connect both the physical and virtual worlds. In groups, students design and build their own interactive hardware/software prototypes and present them in a live demo at the end of term.   

This course introduces students to the concepts and methodology needed to implement and analyze computational models of cognition. It considers the fundamental issues of using a computational approach to explore and model cognition. In particular, this course explores the way that computational models relate to, are tested against, and illuminate psychological theories and data. The course introduces both symbolic and subsymbolic modelling methodologies, and provides practical experience with implementing models. The symbolic part focuses on cognitive architectures, while the subsymbolic part introduces probabilistic models. 

Data processing is an important aspect for every field of study, and Excel is one of the most commonly used methods. Through lecture and homework exercises, this course aims to introduce basic and advanced functions of Excel that are capable of meeting most data processing needs.   

Artificial intelligence in medicine has created tremendous business opportunities recently, creating an ideal environment for AI-Biomedical interdisciplinary specialists to make considerable contributions and significantly impact the world. Intelligent medicine aims to utilize state-of-the-art AI technologies for many medical applications such as accurate disease risk prediction and essential predictors selection, which are for early precise and efficient treatments. This course introduces the vast potential of intelligent medicine, seeking to advance student skills and motivation for AI-Biomedical interdisciplinary science.  

The course also introduces potential partners for future interdisciplinary collaboration to our students and provide opportunities for practical implementations through several carefully designed experiments, which shall demonstrate how to leverage real-world medical resources and related AI technologies. The course includes visits to prestigious companies and institutes and as well as seminars. 

This course examines supervised and unsupervised learning, with emphases on the theoretical underpinnings and on applications in the statistical programming environment R. Topics include linear methods for regression and classification, model selection, model averaging, basic expansions and regularization, kernel smoothing methods, additive models and tree-based methods. We will also provide an overview of neural networks and random forests.