Syllabus
EN
IT
Main referral readings for attending students:
• OECD, (2019), Health at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/4dd50c09-en.
• World Health Statistics 2020: Monitoring Health for the SDGs, Sustainable Development Goals. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO.
• The World Health Report 2013: Research for Universal Health Coverage. WHO, 2013.
• Guinness, L., Wiseman, V., Introduction to Health Economics – Understanding Public Health, McGraw Hill, Open University Press, second edition, 2011.
Main referral readings for non-attending students:
• Routledge Handbook of Global Public Health, 1st Edition.
• Guest, C., Ricciardi, W., Oxford Handbook of Public Health Practice.
Additional materials:
• Required readings, handouts from the teacher, readings from online sources such as www.who.int, www.euro.who.int, www.worldbank.org, etc.
Learning Objectives
The course covers some statistical techniques for supervised and unsupervised learning. The R software for statistical computing will be also introduced and used throughout.
Supervised learning techniques are used to predict a target variable (linear and logistic regression) based on predictors, and/or to assess interrelationships among predictors and a target variable (linear and logistic regression). As an example, suppose you want to predict the risk that a family will be materially deprived next year. This can be done by using data that can be measured at baseline (number of family members, disposable income, health status, etc.) and use these to predict material deprivation for a sample of families with known status. Machine learning methods will also be discussed (classification and regression trees, random forests, shallow and deep neural networks) for supervised learning. Modern applications will be then introduced, where data is extracted from text corpora (natural language processing), images (computer vision), audio tracks.
Learning Outcomes
The course contributes to the achievement of the objectives of the degree course, in line with the professional profiles and employment outlets envisaged, providing students with notions useful for an in-depth and critical understanding of some major debates in the statistical and computer science community, including:
KNOWLEDGE AND UNDERSTANDING:
At the end of the course, students will get to know and understand the basics of statistical and machine learning
APPLYING KNOWLEDGE AND UNDERSTANDING: The course provides tools to understand supervised and unsupervised learning methods. Following the lectures, students will consolidate their knowledge of the fundamental concepts of statistics and data analysis and their ability to independently apply the knowledge they have acquired to socioeconomic problems.
MAKING JUDGEMENTS: the ability to draw independent judgments and conclusions about data analysis is stimulated by highlighting the connections between the concepts developed during the course, the notions acquired in previous courses and the links between these notions and the major contemporary economic problems.
COMMUNICATION SKILLS: By attending the course and interacting with the two lecturers, students will develop their communication skills and their ability to organise and share articulate reasoning, combining notions of statistics and computer science.
LEARNING SKILLS: Through the study of statistical learning the students will acquire the ability to independently analyse and investigate specific topics related to the course contents.
Supervised learning techniques are used to predict a target variable (linear and logistic regression) based on predictors, and/or to assess interrelationships among predictors and a target variable (linear and logistic regression). As an example, suppose you want to predict the risk that a family will be materially deprived next year. This can be done by using data that can be measured at baseline (number of family members, disposable income, health status, etc.) and use these to predict material deprivation for a sample of families with known status. Machine learning methods will also be discussed (classification and regression trees, random forests, shallow and deep neural networks) for supervised learning. Modern applications will be then introduced, where data is extracted from text corpora (natural language processing), images (computer vision), audio tracks.
Learning Outcomes
The course contributes to the achievement of the objectives of the degree course, in line with the professional profiles and employment outlets envisaged, providing students with notions useful for an in-depth and critical understanding of some major debates in the statistical and computer science community, including:
KNOWLEDGE AND UNDERSTANDING:
At the end of the course, students will get to know and understand the basics of statistical and machine learning
APPLYING KNOWLEDGE AND UNDERSTANDING: The course provides tools to understand supervised and unsupervised learning methods. Following the lectures, students will consolidate their knowledge of the fundamental concepts of statistics and data analysis and their ability to independently apply the knowledge they have acquired to socioeconomic problems.
MAKING JUDGEMENTS: the ability to draw independent judgments and conclusions about data analysis is stimulated by highlighting the connections between the concepts developed during the course, the notions acquired in previous courses and the links between these notions and the major contemporary economic problems.
COMMUNICATION SKILLS: By attending the course and interacting with the two lecturers, students will develop their communication skills and their ability to organise and share articulate reasoning, combining notions of statistics and computer science.
LEARNING SKILLS: Through the study of statistical learning the students will acquire the ability to independently analyse and investigate specific topics related to the course contents.
ALESSIO FARCOMENI
Prerequisites
Prerequisite is an introductory statistics and statistical inference course like “Statistical Tools for Decision Making” of the B. A. in Global Governance. Also some math is essential, but only few derivations are made.
Program
Topic 1: Introduction to R Software
Topic 2: Linear Regression
Topic 3: Logistic Regression
Topic 4: Principal Component Analysis
Topic 5: Cluster Analysis
Topic 6: Machine Learning Methods for Supervised Learning
Topic 7: Modern Applications: Text Mining, Image Processing
Topic 2: Linear Regression
Topic 3: Logistic Regression
Topic 4: Principal Component Analysis
Topic 5: Cluster Analysis
Topic 6: Machine Learning Methods for Supervised Learning
Topic 7: Modern Applications: Text Mining, Image Processing
Books
Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to
the students by the course instructors during the course.
the students by the course instructors during the course.
Bibliography
Witten J.D., Hastie T., Tibshirani R. (2014). An Introduction to Statistical Learning with Applications
in R. Springer, Springer Series in Statistics
Chatfield, C. and Collins, A. J. (1981) Introduction to Multivariate Analysis, Chapman & Hall/CRC
Press
Everitt, B. S. and Hothorn, T. (2006) A Handbook of Statistical Analyses Using R. CRC Press.
Available for free at:http://www.ecostat.unical.it/tarsitano/Didattica/LabStat2/Everitt.pdf
Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Second Edition. Springer, Springer Series in Statistics. Available for
free at: https://web.stanford.edu/~hastie/ElemStatLearn/
in R. Springer, Springer Series in Statistics
Chatfield, C. and Collins, A. J. (1981) Introduction to Multivariate Analysis, Chapman & Hall/CRC
Press
Everitt, B. S. and Hothorn, T. (2006) A Handbook of Statistical Analyses Using R. CRC Press.
Available for free at:http://www.ecostat.unical.it/tarsitano/Didattica/LabStat2/Everitt.pdf
Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Second Edition. Springer, Springer Series in Statistics. Available for
free at: https://web.stanford.edu/~hastie/ElemStatLearn/
Teaching methods
In-class teaching
Exam Rules
Assessment for attending students will be based on a written exam. This will include closed and open questions. A midterm written exam will be held.
Non attending students will have to take an oral examination in addition to the written exam.
For assessment purposes, the following scheme will be used:
Unsuitable: major deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgmental skills, the topics are set out inconsistently and with inappropriate language;
18-20: barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent way and with inappropriate/technical language;
21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.
Non attending students will have to take an oral examination in addition to the written exam.
For assessment purposes, the following scheme will be used:
Unsuitable: major deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgmental skills, the topics are set out inconsistently and with inappropriate language;
18-20: barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent way and with inappropriate/technical language;
21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.
MARCO STEFANUCCI
Prerequisites
No formal pre-requisites
Program
The course is divided into two interconnected modules:
1. Definition of Health and Health Indicators
• Definitions of health, health indicators, and main demographic determinants.
2. Natural History of Acute and Chronic Diseases
• Main differences between communicable/infectious and non-communicable/non-infectious diseases (causes, trends, consequences).
3. The Concept of Cause in Medical Sciences
• Causes, risk factors, and health/disease determinants (lifestyles, education, inequalities, social isolation, etc.).
4. Demographic and Epidemiological Transition
• Challenges and opportunities posed by the new scenario (enhanced lifespan, disabilities, migrations, etc.).
5. Global Health
• Challenges posed by the increasing burden of chronic diseases on healthcare systems and new challenges from both new and old infectious diseases (HIV/AIDS, tuberculosis, malaria, SARS-CoV-2 and future pandemics); the urgent need for a global and planetary approach.
6. Health and Economics
• The need for measuring health (DALY, QALY, Global Burden of Disease, etc.).
7. Health Promotion and Protection
• Impact of public policies on health; an interdisciplinary approach.
8. Economic Evaluation in Health
• Preparedness for epidemics and economic issues: the role of WHO and other national and international institutions in managing pandemic diseases; best practices and lessons learned from pandemics (HIV/AIDS, Ebola, SARS-CoV-I and II).
9. Warfare and Health
• Health impact of war, prevention, and containment.
1. Definition of Health and Health Indicators
• Definitions of health, health indicators, and main demographic determinants.
2. Natural History of Acute and Chronic Diseases
• Main differences between communicable/infectious and non-communicable/non-infectious diseases (causes, trends, consequences).
3. The Concept of Cause in Medical Sciences
• Causes, risk factors, and health/disease determinants (lifestyles, education, inequalities, social isolation, etc.).
4. Demographic and Epidemiological Transition
• Challenges and opportunities posed by the new scenario (enhanced lifespan, disabilities, migrations, etc.).
5. Global Health
• Challenges posed by the increasing burden of chronic diseases on healthcare systems and new challenges from both new and old infectious diseases (HIV/AIDS, tuberculosis, malaria, SARS-CoV-2 and future pandemics); the urgent need for a global and planetary approach.
6. Health and Economics
• The need for measuring health (DALY, QALY, Global Burden of Disease, etc.).
7. Health Promotion and Protection
• Impact of public policies on health; an interdisciplinary approach.
8. Economic Evaluation in Health
• Preparedness for epidemics and economic issues: the role of WHO and other national and international institutions in managing pandemic diseases; best practices and lessons learned from pandemics (HIV/AIDS, Ebola, SARS-CoV-I and II).
9. Warfare and Health
• Health impact of war, prevention, and containment.
Books
Attending students:
1. Slides of the course.
2. Reading material distributed by the lecturers
Non attending students (below 80% attendance): will study ONE of the following textbooks:
1. Slides of the course.
2. Reading material distributed by the lecturers
Non attending students (below 80% attendance): will study ONE of the following textbooks:
Bibliography
Main referral readings for attending students:
• OECD, (2019), Health at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/4dd50c09-en.
• World Health Statistics 2020: Monitoring Health for the SDGs, Sustainable Development Goals. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO.
• The World Health Report 2013: Research for Universal Health Coverage. WHO, 2013.
• Guinness, L., Wiseman, V., Introduction to Health Economics – Understanding Public Health, McGraw Hill, Open University Press, second edition, 2011.
Main referral readings for non-attending students:
• Routledge Handbook of Global Public Health, 1st Edition.
• Guest, C., Ricciardi, W., Oxford Handbook of Public Health Practice.
Additional materials:
• Required readings, handouts from the teacher, readings from online sources such as www.who.int, www.euro.who.int, www.worldbank.org, etc.
Teaching methods
The course combines different teaching methods: lectures; seminars; student presentations. The lectures will provide the students with the necessary information and reading guidelines on the phenomena under study, while seminars will see students critically engage with this knowledge and encourage/participate in class debates. Students are expected to attend each class, to come to class prepared and to participate in discussions.
Students will agree the topic of their presentations with the lecturers and give assessed Power-point presentations in which they will critically evaluate the content and argument of a chosen topic and introduce related questions for the class discussion.
Students will agree the topic of their presentations with the lecturers and give assessed Power-point presentations in which they will critically evaluate the content and argument of a chosen topic and introduce related questions for the class discussion.
Exam Rules
Course assessment
The (default )verification of learning takes place exclusively through a final examination which consists of an individual or group presentation as discussed below. The objective of the final examination is to verify the achievement of the course learning outcome. In particular, the examination assesses the student's overall preparation, ability to integrate knowledge of the different parts of the programme, consequentiality of reasoning, analytical ability and autonomy of judgement. In addition, ownership of language and clarity of exposition are assessed, in adherence with the Dublin descriptors.
Minimum score for passing the written test 18 out of 30.
After listening to the presentations, the lecturers communicate the results to the students registered for the examination via the Delphi system.
Students may take the examination on all available dates. there is no roll-call jump.
The examination will be assessed according to the following criteria:
• FAIL: important deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited ability to analyse and synthesise, frequent generalisations and limited critical and judgemental skills, the topics are set out inconsistently and with inappropriate language;
• 18-20: Barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent manner and with inappropriate/technical language;
• 21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
• 24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
• 27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
• 30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.
Course evaluation for attending students:
• In-class presentations (100 %)
• Rules for the presentation: Students can work on their presentations alone or in groups. A group may comprise 2 to 4 students. The students agree on the topic of the presentations with the lecturers, individually or in groups. Each student/group prepares its presentation and emails it to the lecturers at least one day in advance of the day scheduled for class discussion. In the case of group presentations, each group member receives the same final grade.
Course evaluation for non-attending students:
• Final oral exam (100%).
• Rules for the oral exam: the final exam consists of an approximately 20-minute oral test with questions on one of the textbooks indicated above,
The (default )verification of learning takes place exclusively through a final examination which consists of an individual or group presentation as discussed below. The objective of the final examination is to verify the achievement of the course learning outcome. In particular, the examination assesses the student's overall preparation, ability to integrate knowledge of the different parts of the programme, consequentiality of reasoning, analytical ability and autonomy of judgement. In addition, ownership of language and clarity of exposition are assessed, in adherence with the Dublin descriptors.
Minimum score for passing the written test 18 out of 30.
After listening to the presentations, the lecturers communicate the results to the students registered for the examination via the Delphi system.
Students may take the examination on all available dates. there is no roll-call jump.
The examination will be assessed according to the following criteria:
• FAIL: important deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited ability to analyse and synthesise, frequent generalisations and limited critical and judgemental skills, the topics are set out inconsistently and with inappropriate language;
• 18-20: Barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent manner and with inappropriate/technical language;
• 21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
• 24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
• 27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
• 30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.
Course evaluation for attending students:
• In-class presentations (100 %)
• Rules for the presentation: Students can work on their presentations alone or in groups. A group may comprise 2 to 4 students. The students agree on the topic of the presentations with the lecturers, individually or in groups. Each student/group prepares its presentation and emails it to the lecturers at least one day in advance of the day scheduled for class discussion. In the case of group presentations, each group member receives the same final grade.
Course evaluation for non-attending students:
• Final oral exam (100%).
• Rules for the oral exam: the final exam consists of an approximately 20-minute oral test with questions on one of the textbooks indicated above,