TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Doctorate Of Science
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Associate Professor Mustafa Sert
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know deep learning methods such as convolutional neural networks, deep Boltzmann machines, and autoencoders. 2) Know how to design deep architectures for specific problems. 3) Know training and testing of deep neural networks.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	BIL535 - Introduction to Machine Learning
COURSE DEFINITION
COURSE CONTENTS	WEEK TOPICS 1st Week Machine learning basics and applications 2nd Week Overview of artificial neural networks 3rd Week Multilayer perceptron 4th Week Training deep networks 5th Week Convolutional neural networks 6th Week Convolutional neural networks 7th Week Recurrent neural networks 8th Week Midterm 9th Week Deep generative models 10th Week Deep generative models 11th Week Deep reinforcement learning 12th Week Deep reinforcement learning 13th Week Recent research topics and applications on audio-visual and language understanding. 14th Week Recent research topics and applications on audio-visual and language understanding.
RECOMENDED OR REQUIRED READING	1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016 2. K. P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012. 3. Tom Mitchell, \Machine Learning", McGraw-Hill, (1997).
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Problem Solving,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work Preparation for Final exam Course hours 14 3 42 Preparation for Midterm exam 1 30 30 Laboratory (including preparation) Final exam Homework 2 40 80 Project 1 150 150 Total Workload 304 Total Workload / 30 10,13 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No