COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE APPLICATIONS IN PHYSICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ELECTIVE

Offered By

Physics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÜMIT AKINCI

Offered to

Physics

Course Objective

The aim of this course is to provide students with an overview of the theoretical principles and basic applications of artificial neural networks and their basic architectures and applications in physics.

Learning Outcomes of the Course Unit

1   To be able to understand the basic principles of artificial neural networks
2   To be able to recognize the basic learning principles of artificial neural networks
3   Understanding the basic architectures of artificial neural networks
4   To be able to create basic architectures of artificial neural networks
5   To be able to perform physics applications of artificial neural networks such as differential equation solution and optimization

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction: Fundamentals and history of artificial neural networks (ANN)
2 ANN architectures and education process
3 Perceptron Network
4 ADALINE network
5 Multilayer Perceptron Network I
6 Multilayer Perceptron Network II
7 Recurrent Hopfield networks
8 Application of Recurrent Hopfield networks
9 Solution of differential equations by ANN
10 Solution of Lagrange and Hamilton equations of motion by ANN
11 Solution of Schrodinger equation by ANN
12 Optimization problem applications of ANN I
13 Optimization problem applications of ANN II
14 Presentations of asignments

Recomended or Required Reading

Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena, Bartocci Liboni,
Silas Franco dos Reis Alves, Artificial Neural Networks A Practical Course, Springer, 2017

Supplementary Book(s):

Pıerre Peretto, An Introductıon To The Modelıng Of Neural Networks, Cambridge university Press, 1994

Planned Learning Activities and Teaching Methods

Lectures, Question-Answer, Discussion, Tutorials

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG * 0.25 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG * 0.25 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

The student s performance will be evaluated by addition of final exam grade to midterm exams grades and homeworks.

Language of Instruction

Turkish

Course Policies and Rules

Attending at least 70 percent of lectures is mandatory.

Contact Details for the Lecturer(s)

umit.akinci@deu.edu.tr

Office Hours

will be announced

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 12 3 36
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 2 15 30
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 156

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.14133344
LO.242234344
LO.354332345
LO.43132343
LO.53133343

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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