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Advanced Algorithms (2225.YR.015029.1)

General information

Type:

OBL

Curs:

1

Period:

S semester

ECTS Credits:

6 ECTS

Teaching Staff:

Group Teacher Department Language
Year 1 Jordi Nin Guerrero Operaciones, Innovación y Data Sciences ENG

Prerequisites

- Introduction to AI: Programming Foundations
- Computing Foundations

Previous Knowledge

- Python knowledge
- Data structures
- Basic programming

Workload distribution

Lectures and in-class exercises will represent 50% of the workload. The assignments and the preparation for the exams will represent the other 50% of the workload.

COURSE CONTRIBUTION TO PROGRAM

Transforming big data into unique and proprietary business algorithms translates into transforming the entire enterprise. The only way to process data into actionable insight is to create algorithm-based software. Making sense of large amounts of data via algorithms will improve business decision-making, which translates into significant improvements in business performance.

We understand algorithmic business as the industrialized use of complex mathematical algorithms pivotal to driving improved business decisions or process automation for competitive differentiation. The algorithmic business provides the speed and scale to accelerate digital business to deliver even greater impact.

Course Learning Objectives

This subject introduces the necessary algorithmic foundations required to understand basic algorithms techniques and components to solve different business needs related to operations, customer intelligence and marketing.

CONTENT

1. Introduction to algorithms

2. Recursivity

3. Algorithms complexity

4. Brute force algorithms

5. Divide and conquer

6. Searching algorithms

7. Algorithms on trees

8. Algorithms on graphs

Relation between Activities and Contents

1 2 3 4 5 6 7 8
A1: Recursivity                
A2: Divide and conquer                
A3: Algorithms on trees                
A4: Algorithms on graphs                
Mid-term exam                
Final exam                
Attendance                

Methodology

To achieve the objectives, this 10-week course will be based on lectures, class discussions and practice.

Lecture/Discussion. During theoretical lessons, we will introduce the basic concepts for each topic. These sessions will be devoted to the presentation and discussion of frameworks, concepts, and theories.

Practice. In Practice sessions, we will present the general framework and the background tools. Different practical exercises will then be delivered and discussed in class.

ASSESSMENT

ASSESSMENT BREAKDOWN

Description %
A1: Recursivity 10
A2: Divide and conquer 10
A3: Algorithms on trees 10
A4: Algorithms on graphs 10
Mid-term exam 25
Final exam 25
Attendance 10

Assessment criteria

10 % Attendance
40% Assignments
25% Mid-term exam
25% Final exam

Bibliography

Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2022) [1990]. Introduction to Algorithms (4th ed.). MIT Press and McGraw-Hill. ISBN 0-262-04630-X. 1312 pp.

Timetable and sections

Group Teacher Department
Year 1 Jordi Nin Guerrero Operaciones, Innovación y Data Sciences

Timetable Year 1

From 2023/2/1 to 2023/4/19:
Each Wednesday from 10:45 to 13:15. (Except: 2023/3/15 and 2023/4/5)

Wednesday 2023/3/15 from 9:00 to 12:00.

Monday 2023/5/15 from 9:00 to 12:00.