Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Deep Learning
  • Description
  • Description
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  • ECTS Credit Load

Course Introduction Information

Code - Course Title YZM3002 - Deep Learning
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 6.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery This course is offered exclusively in an in-person format.
Prerequisites There are no prerequisites or co-requisites for this course.
Courses Recomended There are no other recommended courses related to this one.
Required or Recommended Resources Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (3rd ed.). O'Reilly Media.
Recommended Reading List Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (3rd ed.). O'Reilly Media.
Assessment methods and criteria 2 midterm exams, 1 final exam
Work Placement This is not suitable for this class.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Multi Layer Perceptron, Backpropagation, Optimizers. Keras.
Week - 2 Advanced CNN Architectures: VGG, Inception, ResNet.
Week - 3 Transfer Learning and Fine-Tuning.
Week - 4 Object Detection. The R-CNN Family.
Week - 5 The YOLO Architecture.
Week - 6 Semantic Segmentation. The U-Net Architecture.
Week - 7 Recurrent Neural Network. The Sequence-to-Sequence (Seq2Seq) Architecture.
Week - 8 The Attention Mechanism.
Week - 9 Introduction to The Transformer Architecture
Week - 10 BERT/GPT and Masked Language Modeling.
Week - 11 Transformer Applications: Text Classification with the Hugging Face Library (Fine-tuning).
Week - 12 Generative Models: Autoencoders (AEs) and Variational Autoencoders (VAEs).
Week - 13 Generative Adversarial Networks (GANs).
Week - 14 The DCGAN Architecture.

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Drill - Practise
  • Problem Solving
  • Competences
  • Productive
  • Rational
  • Questoning
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Concern for quality
  • To work autonomously
  • Elementary computing skills
  • Decision making

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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