Biological neural networks. Artificial neural networks. Basic neuron. Modeling the single neuron. Feed-forward architectures. Recurrent neural networks. Restricted boltzmann machines. Learning algorithms. Back propagation. Contrastive divergence. Optimization and generalization for neural networks. Autoencoders. Deep belief networks. Convolutional neural networks. Long short-term memory networks. Self-attention and transformers. Adversarial networks.
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Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 3.25 | 1 sec · 67 öğr |
| 2024-2025 Fall | 3.34 | 1 sec · 65 öğr |
| 2024-2025 Spring | 3.03 | 1 sec · 64 öğr |
| 2023-2024 Fall | 3.37 | 1 sec · 46 öğr |
| 2022-2023 Fall | 2.67 | 1 sec · 59 öğr |
| 2022-2023 Spring | 3.06 | 1 sec · 30 öğr |
| 2021-2022 Fall | 3.24 | 1 sec · 53 öğr |
| 2021-2022 Spring | 2.69 | 1 sec · 38 öğr |
| 2020-2021 Fall | 2.87 | 1 sec · 50 öğr |
| 2019-2020 Fall | 2.86 | 1 sec · 71 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
All in-class and take-home assignments should be completed and submitted. Grades, except the final exam, should justify a letter grade of D or better. (Note that meeting this criterion does not guarantee that you will eventually pass the course. Your letter grade still depends on your overall performance including the final.)
Generative AI tools (e.g., large language models, coding assistants, and image-generation tools) may be used for take-home assessments (i.e., tutorial/homework and project assignments) to assist with coding, and improving writing. Any use of generative AI must be clearly disclosed, including the name/version of the tool and a brief description of how it was used. Upon request, students should be ready to provide prompts, outputs, and execution logs pertaining to their genAI use for all assignmen
Primer on Linear Algebra, Probability and Optimization for Machine Learning / Introduction to Neural Networks (NNs) Neurons: Biophysical and Mathematical Models Neural Network Structures: Perceptrons Learning Algorithms: Supervised, Unsupervised, Reinforcement Learning Training Single Layer NNs Training Multilayer NNs: Back Propagation, Empirical Risk Minimization Optimization Methods and Generalization Recurrent Neural Networks (RNNs): BP Through Time, LSTM Restricted Boltzmann Machines (RBMs): Contrastive Divergence Autoencoders: Denoising, Contractive, Stacked NNs Deep Learning: Deep Autoencoders, Deep Belief Networks Vision Models: Convolutional and Transformer Networks for Computer Vision Language Models: RNNs and Transformers for Natural Language Processing Generative Modeling: Adversarial Models, Variational Models, Diffusion Probabilistic Models ECTS - Workload Table: Activities Number Hours Workload Course hours 14 3 42 Project (including preparation and presentation if applicable) 4 25 100 Quiz 3 1 3 Preparation for Quiz 3 4 12 Total Workload: 157 Total Workload / 30: 157 / 30 5.23 ECTS Credits of the Course: 5 Type of Course: Lecture - Project - Independent Study Course Material: Written - Multimedia Teaching Methods: Lecture