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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 488DEEP LEARNING3 + 06th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective Deep learning, a branch of machine learning, allows computers to model high-level abstractions from experience (encoded in large-scale labeled and unlabeled data). Recent advances in computing hardware and algorithms have made it a popular tool for artificial intelligence. This course aims at clarifying the theory behind deep learning methods while providing the students with the skills of their effective use in many domains such as computer vision and natural language processing.
Course Content Introduction to Machine Learning, Deep Learning Tools: Caffe, Torch, TensorFlow, Theano, Feedforward Deep Networks, Regularization of Deep or Distributed Models, Optimization for Training Deep Models, Convolutional Networks, Sequence Modeling: Recurrent and Recursive Nets, Structured Probabilistic Models for Deep Learning, Linear Factor Models and Auto-Encoders, Computer Vision Applications, Big Data Applications, Natural Language Processing Applications, Speech Processing Applications.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Basic knowledge of machine learning and deep learning methods.
2Knowledge and experience on how to apply deep learning methods in various domains such as computer vision, natural language processing and big data.
3Knowledge of literature with a focus on recent developments in deeep learning.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001125454213214
LO 002123214523552
LO 003123254512344
Sub Total3611811121248101010
Contribution124344413333

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14456
Mid-terms11515
Final examination11717
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes