ARTIFICIAL NEURAL NETWORKS
- Course
- CMPE545 - ARTIFICIAL NEURAL NETWORKS
- Department
- Computer Engineering - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
Course Description
Introduction to cognitive science. Parallel, distributed problems. Constraint satisfaction. Liopfield model. Supervised vs. unsupervised learning. Single vs. multi-layer perceptions. Static vs. dynamic network architecture. Comparison of neural approaches with parametric and non-parametric statistical methods. Neural network applications.
ARTIFICIAL NEURAL NETWORKS
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Artificial Intelligence, Artificial Neural Networks and Deep Learning |
| Week 2 | Image Formation, Biological Neuron, Artificial Neural Networks |
| Week 3 | Artificial Neural Networks - Single Perceptron, Multi-layer ANNs, backprobagation |
| Week 4 | Convolutional Neural Networks - building blocks |
| Week 5 | Convolutional Neural Networks - applications and Phyton Coding using Tensorflow Keras |
| Week 6 | Long-Short-Term Memory Networks (LSTM) - building blocks and applications |
| Week 7 | LSTM - applications and Phyton Coding using Tensorflow Keras |
| Week 8 | Autoencoders - building blocks and applications |
| Week 9 | Transformer Networks for Natural Language |
| Week 10 | Vision Transformers for Image Recognition |
| Week 11 | Convolutional Vision Transformers |
| Week 12 | Training a Deep Learning Model - in Phyton Tensorflow Keras |
| Week 13 | Hyperparameter Analysis - optimizers, learning rate, batch size, etc. |
| Week 14 | Revision of Concepts |
| Week 15 | Finals |
Reference Books & Course Materials
- 01 Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016. https://www.deeplearningbook.org/
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Based on bachelor's-level qualifications, be able to develop and deepen knowledge at the level of specialization in the same or a different field.
- Should be able to understand and appreciate the interdisciplinary interactions related to their field.
- Should be able to apply expert-level theoretical and practical knowledge acquired in their field.
- Should be able to integrate knowledge from their field with knowledge from other disciplines, interpret it, and generate new knowledge.
- Should be able to resolve problems encountered in their field through the application of appropriate research methods.
- Should be able to independently carry out research or professional work that requires expertise in their field.
- Should be able to develop innovative strategic approaches for resolving complex and unpredictable problems encountered in their field of practice and take responsibility for producing effective solutions.
- Should be able to demonstrate leadership in environments where solving problems related to their field is required.
- Should be able to critically assess the advanced knowledge and skills acquired in their field and manage their own learning processes.
- Should be able to systematically present current developments in their field and their own studies, supported by quantitative and qualitative data, to both disciplinary and non-disciplinary audiences through written, oral, and visual communication.
- Should be able to critically analyze and enhance social relationships and the norms that shape these relationships, and initiate actions aimed at their transformation when necessary.
- Should be able to communicate effectively through oral and written communication in at least one foreign language at the B2 level of the Common European Framework of Reference for Languages (CEFR).
- Should be able to utilize information and communication technologies and relevant computer software at an advanced level appropriate to the requirements of their field.
- Should be able to manage and evaluate the processes of collecting, interpreting, applying, and communicating data related to their field in accordance with social, scientific, cultural, and ethical values, and promote the understanding of these values.
- Should be able to develop strategies, policies, and action plans in areas related to their field and assess the results obtained in accordance with quality assurance processes.
- Should be able to apply the advanced knowledge acquired in their field, along with problem-solving and application skills, in interdisciplinary studies.
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