Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ECE566DEEP LEARNING IN PYTHON3+0+037,514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program ELECTRICAL AND COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Develop knowledge for the fundamentals of machine learning and neural networks
Learn the techniques used for developing deep learning models
Develop skills for implementing machine learning models using libraries of Python
Course Content This course introduces deep learning using Keras library of Python programming language. It covers deep architectures for multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and autoencoders. Students will get hands-on practical knowledge implementing deep learning models and applying them to various machine learning problems.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. ZAFER AYDIN zafer.aydin@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Data representations of neural networks (LO1, LO2) Anatomy of neural networks (LO1, LO2, LO3, LO4) Fundamentals of machine learning (LO1, LO2, LO3, LO4) Training deep neural networks (LO1, LO2, LO3, LO4) Convolutional networks (LO1, LO2, LO3, LO4) Recurrent networks (LO1, LO2, LO3, LO4) Advanced deep learning practices (LO1, LO2, LO3, LO4)

Course Category
Engineering %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
Veri yok

 
ECTS Allocated Based on Student Workload
Veri yok

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain fundamental techniques of deep learning methods
2 Implement deep learning models using libraries of Python
3 Perform simulations and experiments to train, optimize and evaluate deep learning models on real data sets
4 Solve machine learning problems by applying the appropriate deep learning techniques and models

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Data representations of neural networks: tensors, tensor operations, gradient based optimization
2 Anatomy of neural networks: Keras library, codes for classification and regression examples
3 Fundamentals of machine learning: evaluating machine learning models
4 Training deep neural networks: activations, batch normalization, optimization
5 Training deep neural networks: regularization
6 Midterm exam 1
7 Deep learning for computer vision: introduction to convnets, training a convnet from scratch on a small dataset
8 Semester break
9 Deep learning for computer vision: using a pre-trained convnet, visualizing what convnets learn
10 Deep learning for computer vision: segmentation, object detection
11 Midterm exam 2
12 Deep learning for text and sequences: text data, word embeddings, recurrent neurons and layers
13 Deep learning for text and sequences: LSTM and GRU layers, LSTM example, advanced techniques, recurrent dropout, bidirectional RNNs
14 Deep learning for text and sequences: 1D convolution and sequence processing with convnets, combining CNNs and RNNs
15 Advanced deep learning practices: Keras functional API, models as layers, monitoring deep learning models, hyper-parameter optimization, model ensembling
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 5 5 4 4 4 3 4 1 2 2 3
C1 4 5 3 4 3 2 3 1 1 1 2
C2 4 5 3 4 4 3 4 1 1 1 2
C3 5 5 4 4 4 3 5 1 2 2 3
C4 5 5 4 5 5 4 4 1 2 2 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77830&lang=en
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