
Course unit
OPTIMIZATION FOR DATA SCIENCE
SCP7079229, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary 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 
Examination board
Examination board not defined
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) Gradienttype 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, teacherled 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.


