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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
DEEP LEARNING BİL635 - 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITDoctorate Of Science
YEAR OF STUDY-
SEMESTER-
NUMBER OF ECTS CREDITS ALLOCATED10
NAME OF LECTURER(S)Assistant 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 DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTBIL535 ? Introduction to Machine Learning
COURSE CONTENTS
WEEKTOPICS
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 READING1. 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 METHODSLecture,Questions/Answers,Problem Solving,Presentation
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term130
Assignment130
Total(%)60
Contribution of In-term Studies to Overall Grade(%)60
Contribution of Final Examination to Overall Grade(%)40
Total(%)100
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

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