IT SYSTEMS, DATA AND APPLICATIONS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course covers topics related to introduction to information technology. Specifically, the learning outcomes are:
- Introduction to software development processes and technologies.
- Machine learning: terminology, weka installation, use via GUI and API, noise, feature selection and sampling.
- Main aspects of IT systems: HW, SW, internet, security and privacy.
KNOWLEDGE AND UNDERSTANDING:
At the end of the course, the student will have acquired the fundamentals of software systems such as development methodologies, machine learning, HW, SW, internet, security and privacy.
APPLYING KNOWLEDGE AND UNDERSTANDING:
At the end of the class, the student will have acquired the principles of engineering software systems.
MAKING JUDGEMENTS:
At the end of the class, the student will be able to autonomously analyze software systems.
COMMUNICATION SKILLS:
At the end of the class, the student will be able to properly describe software systems.
LEARNING SKILLS:
At the end of the class, the student will be able to autonomously tackle the study of advanced topics in software systems and the solution of new problems.
The course covers topics related to introduction to information technology. Specifically, the learning outcomes are:
- Introduction to software development processes and technologies.
- Machine learning: terminology, weka installation, use via GUI and API, noise, feature selection and sampling.
- Main aspects of IT systems: HW, SW, internet, security and privacy.
KNOWLEDGE AND UNDERSTANDING:
At the end of the course, the student will have acquired the fundamentals of software systems such as development methodologies, machine learning, HW, SW, internet, security and privacy.
APPLYING KNOWLEDGE AND UNDERSTANDING:
At the end of the class, the student will have acquired the principles of engineering software systems.
MAKING JUDGEMENTS:
At the end of the class, the student will be able to autonomously analyze software systems.
COMMUNICATION SKILLS:
At the end of the class, the student will be able to properly describe software systems.
LEARNING SKILLS:
At the end of the class, the student will be able to autonomously tackle the study of advanced topics in software systems and the solution of new problems.
Prerequisites
None
Program
The program consists of two main topics: Introduction to Information Systems and Machine Learning. Each of these two topics is developed in several two-hour lessons, as described below.
-Introduction to IT systems
IT systems
The internet + Communication and networks
Application Software
System Software
System Unit
Input and Output + Secondary storage
Privacy security and ethics
Information systems + Databases
System analysis and design
Programming and languages
-Machine Learning:
Introduction to ML: terminology
Accuracy Metrics & Comparing classifiers accuracy
Feature Selection
WEKA GUI: The explorer
WEKA GUI: The experimenter
Balancing
Cost sensitive classifier
The impact of balancing 1
The impact of balancing 2
The impact of feature selection 1
The impact of feature selection 2
-Introduction to IT systems
IT systems
The internet + Communication and networks
Application Software
System Software
System Unit
Input and Output + Secondary storage
Privacy security and ethics
Information systems + Databases
System analysis and design
Programming and languages
-Machine Learning:
Introduction to ML: terminology
Accuracy Metrics & Comparing classifiers accuracy
Feature Selection
WEKA GUI: The explorer
WEKA GUI: The experimenter
Balancing
Cost sensitive classifier
The impact of balancing 1
The impact of balancing 2
The impact of feature selection 1
The impact of feature selection 2
Books
Computing Essentials 2023, 29th Edition, By Timothy O'Leary and Linda O'Leary and Daniel O'Leary, ISBN10: 1264136781, ISBN13: 9781264136780, McGraw Hill
Witten, Ian H., et al. "Weka: Practical machine learning tools and techniques with Java implementations." (1999).
Witten, Ian H., et al. "Weka: Practical machine learning tools and techniques with Java implementations." (1999).
Bibliography
Tsui, Frank, Orlando Karam, and Barbara Bernal. Essentials of software engineering. Jones & Bartlett Learning, 2022.
Alpaydin, Ethem. Machine learning. MIT press, 2021.
Alpaydin, Ethem. Machine learning. MIT press, 2021.
Teaching methods
Classroom training is structured through various teaching methods to promote comprehensive and interactive learning:
1. Presentation through slides and answering questions: The instructor presents the teaching material using slides to facilitate visual and conceptual understanding. During the lesson, students are encouraged to ask questions and interact, receiving immediate answers that help clarify the concepts discussed.
2. Presentation of case studies: In the classroom, real or hypothetical case studies are discussed and analyzed. This method allows students to apply the theories learned to concrete situations, developing problem-solving skills and critical thinking.
3. Project development: Students are involved in the development of a practical project, working individually or in groups. This practical activity encourages the application of theoretical knowledge and the development of technical and collaborative skills.
1. Presentation through slides and answering questions: The instructor presents the teaching material using slides to facilitate visual and conceptual understanding. During the lesson, students are encouraged to ask questions and interact, receiving immediate answers that help clarify the concepts discussed.
2. Presentation of case studies: In the classroom, real or hypothetical case studies are discussed and analyzed. This method allows students to apply the theories learned to concrete situations, developing problem-solving skills and critical thinking.
3. Project development: Students are involved in the development of a practical project, working individually or in groups. This practical activity encourages the application of theoretical knowledge and the development of technical and collaborative skills.
Exam Rules
The oral exam consists of open-ended questions aimed at ascertaining the level of knowledge of the topics covered.
The exam is the same for attending and non-attending students and evaluates the overall preparation of the student, the ability to integrate the knowledge of the different parts of the program, the consequentiality of the reasoning, the analytical ability and the autonomy of judgment.
Furthermore, language properties and clarity of presentation are evaluated, in compliance with the Dublin descriptors (1. Knowledge and understanding) 2. Ability to apply knowledge and understanding; 3. Making judgments; 4. Learning skills; 5: Communication skills.
The exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
The project does not have its own grade but is used to demonstrate how what is learned during the course can be applied to solve a realistic problem. The project is therefore used to contextualize what has been learned in practice and to inspire the questions of the oral exam.
The exam is the same for attending and non-attending students and evaluates the overall preparation of the student, the ability to integrate the knowledge of the different parts of the program, the consequentiality of the reasoning, the analytical ability and the autonomy of judgment.
Furthermore, language properties and clarity of presentation are evaluated, in compliance with the Dublin descriptors (1. Knowledge and understanding) 2. Ability to apply knowledge and understanding; 3. Making judgments; 4. Learning skills; 5: Communication skills.
The exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
The project does not have its own grade but is used to demonstrate how what is learned during the course can be applied to solve a realistic problem. The project is therefore used to contextualize what has been learned in practice and to inspire the questions of the oral exam.