COURSE INFORMATION Course Title: INTRODUCTION TO NEURAL NETWORKS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
ECE 433	B	3	3	2	0	4	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Carlo Ciulla cciulla@epoka.edu.al , Thursdays 8.45 am - 10.45 am
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Master of Science in Computer Engineering (2 years)
Classroom and Meeting Time:
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:
Course Description:	This course offers the basic knowledge in artificial neural networks and machine learning, spanning from the introductory concepts to well established and cardinal learning algorithms. The backpropagation learning algorithm is taught along with the delta rule for the weights’ adjustments of the neural network. Also, traditional nets are presented along with modern convolutional neural networks and deep neural networks.
Course Objectives:	This course gives an overview of both the foundational ideas and the recent advances in neural net algorithms. Roughly the first 2/3 of the course focuses on supervised learning — training the network to produce a specified behavior when one has lots of labeled examples of that behavior. The last 1/3 focuses on unsupervised learning — the correct behavior isn’t specified by hand, but the goal is to discover interesting regularities in the data.

BASIC CONCEPTS OF THE COURSE

1	Introduction
2	Linear Regression
3	Logistic Regression models using Maximum Likelihood
4	Perceptron learning
5	Backpropagation
6	Neural language models and optimization
7	Recurrent neural nets
8	Convolutional nets
9	Probabilistic models
10	Autoencoders

COURSE OUTLINE

Week	Topics
1	Introduction
2	Linear Regression
3	Logistic Regression models using Maximum Likelihood.
4	Perceptron learning
5	Backpropagation
6	Neural language models and optimization
7	Recurrent neural nets
8	Convolutional nets
9	Probabilistic models
10	Project progress report presentation
11	Boltzmann machines
12	Autoencoders
13	Bayesian neural networks
14	Reinforcement learning

Prerequisite(s):
Textbook(s):	Pattern Recognition and Machine Learning by Christopher M. Bishop
Additional Literature:	Deep Learning by Yoshua Bengio, Ian Goodfellow, and Aaron Courville
Laboratory Work:	2
Computer Usage:	C/C++
Others:	No

COURSE LEARNING OUTCOMES

1	Understand the learning and generalisation issue in neural computation.
2	Understand the basic ideas behind most common learning algorithms for multilayer perceptrons, radial-basis function networks, and Kohonen self-organising maps.
3	Implement common learning algorithms using an existing package.
4	Apply neural networks to classification and recognition problems.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Master of Science in Computer Engineering (2 years) Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	4
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	4
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	5
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	5
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	5
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Project	1	50
Quiz	2	5
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	3	48
Mid-terms			0
Assignments	1	40	40
Final examination	1	25	25
Other	1	26.5	26.5
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5

CONCLUDING REMARKS BY THE COURSE LECTURER

This section should be completed after the end of the semester