BSCS_AIE 481: Deep Learning and Neural Networks
Course Content
Week 1: Introduction to Neural Networks
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Introduction to Neural Networks
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LO1: Define the basic concepts and architecture of neural networks.
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LO2: Explain the biological inspiration and functioning of artificial neural networks.
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LO3: Identify the key components of a neural network architecture.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple-Choice Questions
Week 2: Mathematical Foundations
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Mathematical Foundations
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LO1: Recall fundamental linear algebra and calculus concepts used in neural networks.
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LO2: Explain optimization techniques used in neural network training.
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LO3: Apply mathematical concepts to analyze neural network computations.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple-Choice Questions
Week 3: Training Neural Networks
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Training Neural Networks
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LO1: Explain techniques for evaluating neural network models.
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LO2: Apply hyperparameter tuning methods to improve model performance.
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LO3: Analyze the impact of optimization techniques on model training performance.
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Multiple-Choice Questions
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Short Answer Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 4: Deep Learning Architectures
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Deep Learning Architectures
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LO1: Define different deep learning architectures such as feedforward networks and CNNs.
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LO2: Explain the structural differences between various neural network architectures.
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Multiple-Choice Questions
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LO3: Analyze how architecture selection affects model performance.
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Scenario Based Multiple Choice Questions
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True/False Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 5: Convolutional Neural Networks (CNNs)
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Convolutional Neural Networks (CNNs)
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LO1: Describe the architecture and components of CNNs.
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LO2: Apply CNN models to image processing tasks.
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LO3: Evaluate CNN performance in image recognition problems.
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Multiple-Choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 6: Recurrent Neural Networks (RNNs)
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Recurrent Neural Networks (RNNs)
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LO1: Explain the structure and working of RNNs and LSTMs.
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LO2: Apply RNN models to sequential data problems.
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LO3: Analyze the advantages and limitations of RNN architectures.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple-Choice Questions
Week 7: Generative Adversarial Networks (GANs)
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Generative Adversarial Networks (GANs)
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LO1: Define the concept and structure of GANs.
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LO2: Explain the interaction between generator and discriminator networks.
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LO3: Evaluate GAN performance in image generation tasks.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple-Choice Questions
Week 8: Midterm Test or Assignment
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Midterm Test or Assignment
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Multiple-Choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Short Answer Questions
Week 9: Transfer Learning and Pretrained Models
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Transfer Learning and Pretrained Models
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LO1: Describe the concept of transfer learning in deep learning models.
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LO2: Apply pretrained models for solving new machine learning tasks.
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LO3: Evaluate the effectiveness of fine-tuning techniques in model performance.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple-Choice Questions
Week 10: Deep Learning for NLP
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Deep Learning for NLP
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LO1: Identify deep learning models used in natural language processing.
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LO2: Explain how neural networks process textual data in NLP tasks.
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LO3: Apply deep learning models to perform NLP tasks such as classification or sentiment analysis.
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
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Multiple choice Questions
Week 11: Advanced Topics in Neural Networks
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Advanced Topics in Neural Networks
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LO1: Describe reinforcement learning concepts in neural networks.
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LO2: Analyze unsupervised learning techniques used in neural networks.
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LO3: Evaluate advanced neural network models for complex learning tasks.
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Multiple choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 12: Model Evaluation and Tuning
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Model Evaluation and Tuning
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LO1: Explain techniques for evaluating neural network models.
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LO2: Apply hyperparameter tuning methods to improve model performance.
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LO3: Analyze issues such as overfitting and underfitting in deep learning models.
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True/False Questions
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Multiple choice Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 13: Ethical Considerations in AI
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Ethical Considerations in AI
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LO1: Identify ethical issues related to deep learning and AI systems.
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LO2: Explain the impact of bias and fairness in AI models.
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LO3: Evaluate ethical implications of deploying deep learning systems.
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Multiple choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 14: Final Project Development
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Final Project Development
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LO1: Apply deep learning techniques to develop a project solution.
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LO2: Analyze project results using appropriate evaluation metrics.
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LO3: Design an end-to-end deep learning application.
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Multiple choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 15: Final Project Presentation and Review
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Final Project Presentation and Review
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LO1: Present deep learning project outcomes effectively.
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LO2: Evaluate peer projects based on technical and methodological criteria.
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LO3: Justify design decisions used in the developed deep learning models.
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Multiple choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Key Term and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 16: Final Test or Project
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Final Test or Project
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Multiple choice Questions
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True/False Questions
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Scenario Based Multiple Choice Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
