First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
DATA SCIENCE
Course unit
OPTIMIZATION FOR DATA SCIENCE
SCP7079229, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
DATA SCIENCE
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination OPTIMIZATION FOR DATA SCIENCE
Website of the academic structure http://datascience.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge FRANCESCO RINALDI MAT/09

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7080718 OPTIMIZATION FRANCESCO RINALDI IN2371
INP7080718 OPTIMIZATION FRANCESCO RINALDI IN2371
INP7080718 OPTIMIZATION FRANCESCO RINALDI IN2371
INP7080718 OPTIMIZATION FRANCESCO RINALDI IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses MAT/09 Operational Research 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: Basic knowledge of
- Real Analysis and Calculus;
- Linear Algebra;
- Probability theory.
Target skills and knowledge: Understanding optimization models and methods for Data Science.
More specifically:
1) Understanding theoretical properties useful for building up
mathematical models in data science.
2) Analyzing and using mathematical models for solving data science problems.
3) Developing and/or using effective solution methods for specific
data science problems.
Examination methods: - Written exam
- Homeworks
- Project (Optional)

1) Homeworks will periodically be assigned based on reading and lecture and will be due at given deadlines.

2) Written exam consists of 5 open questions.

3) Project (optional) can be requested to better analyze specific topics.

Written exams represents 85% of grade.
Homeworks represent 15% of grade.
Project gives an increase (1 up to 3 points) of the grade.
Assessment criteria: The student has to prove:
- his/her understanding of the topics covered during the course;
- his/her knowledge of the theoretical results;
- his/her ability to properly use the models and the algorithms presented in the course.
Course unit contents: 1. Linear optimization: Theory and algorithms
(a) LP models for Data science;
(b) Duality;
(c) Simplex method;
(d) Interior point methods;

2. Convex sets and convex functions
(a) Convexity: basic notions;
(c) Convex functions: Basic notions and properties (gradients, Hessians..);

3. Unconstrained convex optimization
(a) Models in data science;
(b) Characterizations of optimal sets;
(c) Gradient-type methods;
(d) Block coordinate gradient methods;
(e) Stochastic optimization methods;

4. Constrained convex optimization
(a) Models in data science;
(b) Characterizations of optimal sets;
(c) Polyhedral approximation methods;
(d) Gradient projection methods;

5. Large scale network optimization
(a) Network models in data science;
(b) Methods for distributed optimization.
Planned learning activities and teaching methods: - lectures, teacher-led discussions and assignments;
- Lecturer will be using blackboard and slides;
- Notes and slides will be made available on the moodle platform.
Additional notes about suggested reading: - Notes and slides written by the lecturer.
Textbooks (and optional supplementary readings)
  • Nesterov, Yurii, Introductory lectures on convex optimization: A basic course.. --: Vol. 87. Springer Science & Business Media, 2013. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)

Sustainable Development Goals (SDGs)
Quality Education