PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 488	DEEP LEARNING	3 + 0	7th Semester	5

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

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

COURSE'S CONTRIBUTION TO PROGRAM

Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Assignments	4	6	24
Mid-terms	1	10	10
Final examination	1	14	14
Presentation / Seminar Preparation	1	14	14
Report / Project	1	26	26
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

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