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Main
UQU Programs
Science in Artificial Intelligence

Advanced Deep Learning

AI4203
3 hours English

Advanced Deep Learning

Advanced Deep Learning
AI3202 Introduction to Deep Learning
3 hours One requisite
1 Review of deep learning fundamentals and introduction to advanced architectures
2 Attention mechanisms: theory, types (self-attention, multi-head attention), and applications
3 Transformer architecture: encoder-decoder structure, positional encoding, and self-attention computation
4 Applications of Transformers in NLP (e.g., BERT, GPT)
5 Applications of Transformers in computer vision and speech recognition (e.g., Vision Transformers - ViT)
6 Advanced generative models: GANs, VAEs, and autoregressive models
7 Optimization techniques for advanced models (e.g., AdamW, learning rate scheduling, regularization)
8 Scalability and Emerging Trends in Deep Learning
9 Explainability, Ethics, and Future Directions in Deep Learning
1.1 Mapped to: K1

Describe the theory and mechanisms behind advanced deep learning methods.

Teaching Strategy Lectures, discussions
Assessment Methods Exams
1.2 Mapped to: K2

Explain the Transformer architecture and its significance in modern AI.

Teaching Strategy Lectures, discussions
Assessment Methods Exams
2.1 Mapped to: S1

Apply strategies to design and implement a project using advanced deep-learning techniques

Teaching Strategy Lectures, discussions
Assessment Methods Exams, assignments, Project
2.2 Mapped to: S2

Critically review and integrate advanced deep learning solutions to address real-life problems

Teaching Strategy Lectures, discussions
Assessment Methods Exams, assignments, Project
3.1 Mapped to: V1

Demonstrate ability to apply ethical behavior in the project

Teaching Strategy Lectures, discussions
Assessment Methods Assignments, Project
3.2 Mapped to: V2

Demonstrate ability to recognize professional responsibilities and make informed judgments based on legal and ethical principles.

Teaching Strategy Lectures, discussions
Assessment Methods Assignments, Project
Assessment
Weight
Programming assignments
20%
Quizzes
10%
Midterm
30%
Final
40%
Total
100%