tanszek:oktatas:applied_machine_learning
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| tanszek:oktatas:applied_machine_learning [2026/01/21 12:45] – created nasraldeen | tanszek:oktatas:applied_machine_learning [2026/01/21 12:51] (current) – nasraldeen | ||
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| | Credit Hours | 2+2 | | | Credit Hours | 2+2 | | ||
| |Attendance Requirement| Students must attend 75% of classes and pass two midterm pre-exams during the semester to obtain the necessary signature for eligibility to take the final exam| | |Attendance Requirement| Students must attend 75% of classes and pass two midterm pre-exams during the semester to obtain the necessary signature for eligibility to take the final exam| | ||
| - | |Examination| The final exam is required in both written and oral forms during the examination period announced by the faculty. | + | |Examination| The final exam is required in both written and oral forms during the examination period announced by the faculty. Remark: The responsible tutors deliver the topics and lecture presentations during the semester. The PPT lecture presentations or a book in PDF format will be handed to the students via Neptune or email before the pre-exams and the final exam| |
| - | Remark: The responsible tutors deliver the topics and lecture presentations during the semester. The PPT lecture presentations or a book in PDF format will be handed to the students via Neptune or email before the pre-exams and the final exam| | + | |
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| | Week 1| Introduction to Machine Learning: ML, DL, and ANN-Types of ML| | | Week 1| Introduction to Machine Learning: ML, DL, and ANN-Types of ML| | ||
| - | | Week 2 & Week 3| Fundamentals of Machine Learning Algorithms: Gradient descent basics, Simple prediction/ | + | | Week 2 & Week 3| Fundamentals of Machine Learning Algorithms: Gradient descent basics, Simple prediction/ |
| | Week 4| Introduction to Artificial Neural Networks: Biological neuron vs. artificial neuron-Perceptron model & limitations| | | Week 4| Introduction to Artificial Neural Networks: Biological neuron vs. artificial neuron-Perceptron model & limitations| | ||
| - | | Week 5 | Multilayer Perceptron (MLP): Network architecture| | + | | Week 5| Multilayer Perceptron (MLP): Network architecture| |
| - | | Week 6 | Training ANNs: Backpropagation, | + | | Week 6| Training ANNs: Backpropagation, |
| - | | Week 7 | Optimization & Regularization: | + | | Week 7| Optimization & Regularization: |
| - | | Week 8 | Using Frameworks: Training pipeline overview, TensorFlow| | + | | Week 8| Using Frameworks: Training pipeline overview, TensorFlow| |
| - | | Week 9 | Convolutional Neural Networks (CNNs): Popular CNN architectures| | + | | Week 9| Convolutional Neural Networks (CNNs): Popular CNN architectures| |
| - | | Week 10 | Recurrent Neural Networks (RNNs) & Sequence Models| | + | | Week 10| Recurrent Neural Networks (RNNs) & Sequence Models| |
| - | | Week 11 | Model Evaluation & Deployment: Evaluation metrics, accuracy, precision, recall| | + | | Week 11| Model Evaluation & Deployment: Evaluation metrics, accuracy, precision, recall| |
| - | | Week 12 | ANN Applications in Real-World Domains| | + | | Week 12| ANN Applications in Real-World Domains| |
| - | | Week 13 | Model: Image classifier, Fraud detection model, Analysers| | + | | Week 13| Model: Image classifier, Fraud detection model, Analysers| |
| - | | Week 14 | Final Project Presentation & Review: Students present ML-ANN-based projects that incorporate advanced ANN topics, such as XAI and GANs| | + | | Week 14| Final Project Presentation & Review: Students present ML-ANN-based projects that incorporate advanced ANN topics, such as XAI and GANs| |
tanszek/oktatas/applied_machine_learning.1768999542.txt.gz · Last modified: 2026/01/21 12:45 by nasraldeen