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Course unit
OPTIMIZATION FOR DATA SCIENCE
SCP7079229, A.A. 2017/18

Information on the course unit
Degree course Second cycle degree in DATA SCIENCE SC2377, Regulation 2017/18, A.Y. 2017/18 6.0 OPTIMIZATION FOR DATA SCIENCE http://datascience.scienze.unipd.it/2017/laurea_magistrale Department of Mathematics No English PADOVA

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

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

Mode of delivery (when and how)
Period Second semester 1st Year frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours Individual
study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 26/02/2018 01/06/2018

Examination board
Board From To Members of the board
1 Optimization for Data Science - a.a. 2017/2018 01/10/2017 30/09/2018 RINALDI FRANCESCO (Presidente)
DI SUMMA MARCO (Membro Effettivo)
CONFORTI MICHELANGELO (Supplente)
DE FRANCESCO CARLA (Supplente)
DE GIOVANNI LUIGI (Supplente)

Syllabus
 Prerequisites: Basic knowledge of - Real Analysis and Calculus; - Linear Algebra. Target skills and knowledge: Understanding optimization models and methods for Data Science. Examination methods: - Written exam - Project (Optional) Assessment criteria: The student has to prove his/her understanding of the theoretical results and the algorithms presented in the course, and his/her capability to solve exercises. Course unit contents: 1. Linear optimization: Theory and algorithms (a) Lp models for Data science; (b) Duality (Farkas); (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; 4. Constrained convex optimization (a) Models in data science; (b) Characterizations of optimal sets; (c) Poyhedral approximation methods; (d) Gradient projection methods; 5. Large scale network optimization (a) Network models in data science; (b) Clustering methods. Planned learning activities and teaching methods: Lessons, including exercises Additional notes about suggested reading: - Notes written by the lecturer - Text books will be specified during the course Textbooks (and optional supplementary readings)