Course Name | Code | Semester | T+U Hours | Credit | ECTS |
---|---|---|---|---|---|
Introduction To Deep Learning | ISE 469 | 7 | 3 + 0 | 3 | 5 |
Precondition Courses | |
Recommended Optional Courses | |
Course Language | Turkish |
Course Level | Bachelor's Degree |
Course Type | Optional |
Course Coordinator | Doç.Dr. DEVRİM AKGÜN |
Course Lecturers | Doç.Dr. DEVRİM AKGÜN, |
Course Assistants | |
Course Category | Field Proper Education |
Course Objective | To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications. |
Course Content | 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. |
# | Course Learning Outcomes | Teaching Methods | Assessment Methods |
---|---|---|---|
1 | Knows the basic topics about deep learning | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, Performance Task, |
2 | Knows 1D and 2D convnet-convolution layers. | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, Performance Task, |
3 | Uses open source libraries for deep learning | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, Performance Task, |
4 | Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, Performance Task, |
Week | Course Topics | Preliminary Preparation |
---|---|---|
1 | Overview of artificial intelligence, machine learning and deep learning | Slides |
2 | Mathematical foundations, Gradient descent algorithms, loss functions, backpropagation. | Slides |
3 | Tensor operations with Python, Keras deep learning library | Slides |
4 | Multi-label classification, Regression | Slides |
5 | Data preprocessing, overfitting prevention, weight regularization, dropout | Slides |
6 | 2-Dimensional Convolution (conv2D) Neural Networks (convnets), pooling | Slides |
7 | Augmentation of image data, pretrained networks | Slides |
8 | Fine tuning, convolution filters visualization | Slides |
9 | Deep learning with text data, Embedding layers | Slides |
10 | Recurrent neural networks, LSTM and GRU layers | Slides |
11 | Array processing with 1D convnets | Slides |
12 | Keras functional API, Multiple input or multiple output models | Slides |
13 | Generative deep learning | Slides |
14 | Presentations on state of the art topics in deep learning | Slides |
Resources | |
---|---|
Course Notes | <p>Weekly uploaded slides</p> |
Course Resources | Chollet, Francois. Deep learning with python. Manning Publications Co., 2017. Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. |
Order | Program Outcomes | Level of Contribution | |||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
1 | X | ||||||
2 | X | ||||||
3 | X | ||||||
4 | X | ||||||
5 | X | ||||||
6 | |||||||
7 | X | ||||||
8 | |||||||
9 | X | ||||||
10 | |||||||
11 | |||||||
12 |
Evaluation System | |
---|---|
Semester Studies | Contribution Rate |
1. Ara Sınav | 60 |
1. Ödev | 10 |
2. Ödev | 10 |
1. Proje / Tasarım | 20 |
Total | 100 |
1. Final | 50 |
1. Yıl İçinin Başarıya | 50 |
Total | 100 |
ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
---|---|---|---|
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 |
Total Workload | 125 | ||
Total Workload / 25 (Hours) | 5 | ||
dersAKTSKredisi | 5 |