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Main
UQU Programs
Science in Artificial Intelligence

Introduction to Deep Learning

AI3202
3 hours English

Introduction to Deep Learning

Introduction to Deep Learning
AI3201 Machine Learning
3 hours Two requisites
1 Fundamentals of deep learning
2 Basics of neural networks, forward and backpropagation / Training
3 Deep feedforward neural networks (preprocessing, weight / initialization, batch normalization, regularization, optimization, and learning rates)
4 Convolutional neural networks (CNN): convolutions, dropout 2D, / batch norm 2D, common architectures, transfer learning
5 Sequence Modeling: Recurrent neural networks (RNN), long-term short memory (LTSM) networks, Attention, and transformers
6 Generative models: autoregressive models, variational autoencoder, / generative adversarial networks (GANs)
1.1 Mapped to: K1

Describe the theory behind a deep learning method

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

Understand deep learning architectures

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

Assess a deep learning model performance

Teaching Strategy Lectures, discussions
Assessment Methods Exams
1.4 Mapped to: K3

Understand the deployment of the deep learning models

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

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

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

Critically review and integrate 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%