Epistemology
A.Y. 2020/2021
Learning objectives
The course aims at developing the basics of probability logic and its application to problems of inductive inference and rational decision. It also aims at giving students an appreciation of how those topics should contribute towards providing a sound methodology for scientific enquiry in the age of "big data"
Expected learning outcomes
Knowledge and understanding
At the end of the course, students
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts and reasoning tools of classical logic
- know the central concepts in the foundations of probability
- know the central concepts in rational decision theory
Ability to apply knowledge and understanding
At the end of the course, students
- can read and evaluate the scientific literature on inductive reasoning and rational decision
- can apply the tools of logic and probability learnt in the course to solve scientific, philosophical and practical problems
- appreciate the relevance of inductive logic in the current debate on the datacentric revolution in the methodology of the natural
At the end of the course, students
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts and reasoning tools of classical logic
- know the central concepts in the foundations of probability
- know the central concepts in rational decision theory
Ability to apply knowledge and understanding
At the end of the course, students
- can read and evaluate the scientific literature on inductive reasoning and rational decision
- can apply the tools of logic and probability learnt in the course to solve scientific, philosophical and practical problems
- appreciate the relevance of inductive logic in the current debate on the datacentric revolution in the methodology of the natural
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
The course will be delivered with a combination of synchronous and asynchronous modalities. Two of there three weekly lectures will be devoted to asynchronous voice-over-slides presentations; the third one will be devoted to synchronous lectures/online meetings during which topics presented during the week will be discussed and knowledge acquisition will be tested. Both synchronous and asynchronous lectures, as well as all teaching material, will be made available through the Ariel website for this course. For the intermediate and final tests, we will use the secure site exam.net. The simple user instructions will be published in the Ariel website.
Course syllabus
1. Reasoning with data
- Data and its meaning
- Elementary descriptive statistics
- Elementary probability
2. The epistemology of reasoning with data: induction
- Hume's problem of induction
- Induction and knowledge
- More data vs better data
3. The logic of reasoning under uncertainty
- Introduction to Probability logic
- Coherence
- Conditional probability in a logical setting
- Data and its meaning
- Elementary descriptive statistics
- Elementary probability
2. The epistemology of reasoning with data: induction
- Hume's problem of induction
- Induction and knowledge
- More data vs better data
3. The logic of reasoning under uncertainty
- Introduction to Probability logic
- Coherence
- Conditional probability in a logical setting
Prerequisites for admission
Good command of classical propositional logic
Teaching methods
Frontal and flipped lectures and assignments. The approach will be problem-oriented and students will be trained to learn by solving basic problems and exercises.
Teaching Resources
Lecture notes and material will be made available by the instructor
Assessment methods and Criteria
Learning assessment will be through a written exam at the end of the course. Students attending the course can opt for a mid-term examinations at the end of each module.
The exam worth 6 CFU will be on the topics 1-4 (the first 40
hours). The exam worth 9 CFU will be on all the course topics (a
total of 60 hours).
The text of the partial and final exams includes open questions (30%), exercises (50%) and multiple choices tests (20%). These proportions broadly reflect their contribution to the composition of the final score. Multiple choice tests and open questions are aimed to broadly verify the understanding of concepts and definitions taught during the course whereas exercises are designed to evaluate problem solving skills.
The exam worth 6 CFU will be on the topics 1-4 (the first 40
hours). The exam worth 9 CFU will be on all the course topics (a
total of 60 hours).
The text of the partial and final exams includes open questions (30%), exercises (50%) and multiple choices tests (20%). These proportions broadly reflect their contribution to the composition of the final score. Multiple choice tests and open questions are aimed to broadly verify the understanding of concepts and definitions taught during the course whereas exercises are designed to evaluate problem solving skills.
Unita' didattica A
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 3
Lessons: 20 hours
Unita' didattica B
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 3
Lessons: 20 hours
Unita' didattica C
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 3
Lessons: 20 hours
Professor(s)
Reception:
Friday 8:30-11:30
Second Floor, Cortile Ghiacchiaia. Please email me to secure your slot.