| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Introductıon To Deep Learnıng | ISE 469 | 7 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Dr.Öğr.Üyesi BURCU ÇARKLI YAVUZ |
| Dersi Verenler | Dr.Öğr.Üyesi BURCU ÇARKLI YAVUZ, Dr.Öğr.Üyesi SUNUSİ BALA ABDULLAHİ, |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Diğer |
| Dersin Amacı | To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications. |
| Dersin İçeriği | Introduction to Artificial incelligence, machine learning and deep learning. Mathematical background. Gradient Descent algorithms, loss functions, backpropogation. Keras deep learning library, tensor operations in Python. Multi label classification, Regression. Data prepocessig, overfitting, weight regularization, dropout. 2D Convolution (conv2D) neural networks (convnets), pooling. Image data augmentation. Pretrained networks, fine tuning Visualizing convnet filters. Deep learning with text data. Emedding layers. Recurrent neural networks, LSTM and GRU layers. 1D convnets for sequence data. Keras functional API, Multi input multi output models. Generative deep learning. Presentations about state of the art topics. |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Knows the basic topics about deep learning | Lecture, Question-Answer, Discussion, Sightseeing, Observation, | |
| 2 | Knows 1D and 2D convnet-convolution layers. | Lecture, Question-Answer, Discussion, Sightseeing, Observation, | |
| 3 | Uses open source libraries for deep learning | Lecture, Question-Answer, Discussion, Sightseeing, Observation, | |
| 4 | Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers | Lecture, Question-Answer, Discussion, Sightseeing, Observation, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Introduction to Deep learning: Overview of Machine Learning, Deep Learning and Artificial Intelligence. | Slides |
| 2 | Review of Mathematical Foundations: Linear Algebra, Probability, Information Theory. | Slides |
| 3 | Gradient-based optimization: Overview of gradient descent algorithms, loss functions, and backpropagation. | Slides |
| 4 | Overview of Machine Learning: Feedforward neural networks, Tree-based methods, etc. and ML-based packages. | Slides |
| 5 | Basic concept of deep learning: Deep forward network and backpropagation, Regularization for deep learning, Keras deep learning library (Keras API). | Slides |
| 6 | Deep learning training fundamentals: data preprocessing, preventing overfitting, weight regularization, dropout, pooling mechanisms, and optimization for training deep models. | Slides |
| 7 | Convolutional Neural Network (CNN): 2-D Conv., CNN pooling and image classification. | Slides |
| 8 | Sequence learning: 1-D CNN, RNN, LSTM, GRU, and Natural Language generation. | Slides |
| 9 | Embedding representations: Embedding layers with text classification. | Slides |
| 10 | Transfer Learning and Fine tuning. | Slides |
| 11 | Autoencoders | Slides |
| 12 | Advanced architectures: Attention, Attention CNN, Transformers,LLMs etc. | Slides |
| 13 | Research presentations. | Slides |
| 14 | Research presentations. | Slides |
| Kaynaklar | |
|---|---|
| Ders Notu | Weekly uploaded slides |
| Ders Kaynakları | Chollet, Francois. Deep learning with python. Manning Publications Co., 2017. Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| # | Ders Öğrenme Çıktılarının Program Çıktılarına Katkısı |
|---|---|
| 1 | Knows the basic topics about deep learning |
| 2 | Knows 1D and 2D convnet-convolution layers. |
| 3 | Uses open source libraries for deep learning |
| 4 | Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers |
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 60 |
| 1. Ödev | 10 |
| 2. Ödev | 10 |
| 1. Proje / Tasarım | 20 |
| Toplam | 100 |
| 1. Final | 50 |
| 1. Yıl İçinin Başarıya | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 2 | 32 |
| Mid-terms | 1 | 10 | 10 |
| Quiz | 1 | 6 | 6 |
| Performance Task (Application) | 2 | 7 | 14 |
| Final examination | 1 | 15 | 15 |
| Toplam İş Yükü | 125 | ||
| Toplam İş Yükü / 25 (Saat) | 5 | ||
| dersAKTSKredisi | 5 | ||