Added module 5 lectures in markdown

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diff --git a/tutorials/module_5/convolutional_neural_networks_for_engineering_images.md b/tutorials/module_5/convolutional_neural_networks_for_engineering_images.md
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+Lecture 7: Convolutional Neural Networks for Engineering Images
+
+- Why fully connected NNs fail on images
+- Convolution: filters, feature maps
+- Pooling & downsampling
+- CNN pipeline overview
+- Applications:
+	- Crack detection in metals/concrete
+	- manufacturing defect identification
+	- thermal-image pattern recognition
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diff --git a/tutorials/module_5/data_for_engineering.md b/tutorials/module_5/data_for_engineering.md
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+Lecture 5: Data for Engineering: Features, Labels, Normalization, Metrics
+
+- Train/validaiton/test splits
+- Normalization & scaling
+- Features engineering basics
+- Metrics: accuracy, MSE, ROC, F1
+- Engineering-specific constraints:
+	- sensor noise
+	- unit consistency
+	- small datasets
+	- measurement bias
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diff --git a/tutorials/module_5/failure_and_fatigue_prediction.md b/tutorials/module_5/failure_and_fatigue_prediction.md
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+Lecture 8: AI for Failure & Fatigue Prediction
+
+- Using time-series sensor data
+- Features: peaks, cycles, RMS, stress/strain sequences
+- Classical models vs ANN-based predictors
+- Introduction to RUL (Remaining Useful Life) concepts
+- Real-world challenges:
+	- limited failure examples
+	- imbalanced data
+	- saftey requirements and false positives
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diff --git a/tutorials/module_5/how_neural_networks_learn.md b/tutorials/module_5/how_neural_networks_learn.md
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+Lecture 3: How Neural Networks Learn
+
+- Loss functions
+- Gradient descent & optimization
+- Backpropagation concept
+- Underfitting, overfitting, regularization
+- Hyperparametersupervised Learning: Regression for engineering systems
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diff --git a/tutorials/module_5/introduction_to_ai.md b/tutorials/module_5/introduction_to_ai.md
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+Lecture 1: Introduction to AI
+
+- Historical context: Expert systems -> machine learning -> deep learning
+- What is AI? Deep learning? Neural Networks?
+- What makes AI useful in engineering: automation, prediction, anomaly detection
+- Types of engineering problems where ai is superior.
+- Application: 
+	- Predictive maintenance
+	- Quality control
+	- CFD/FEA surrogate models
+- Limitations, misconceptions and ethical considerations
+- Short demo: Show a simple trained model making prediction (linear regression)
+
diff --git a/tutorials/module_5/neural_networks_core_concepts.md b/tutorials/module_5/neural_networks_core_concepts.md
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+Lecture 2: Neural Networks: Core Concepts
+
+- Perceptron, weights, bias
+- Multi-layer perceptron (MLP)
+- Activation functions
+- Forward pass (conceptual math only)
+- Why neural networks can learn nonlinear engineering behavior
+- Small example: predicting a nonlinear spring force curve
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diff --git a/tutorials/module_5/supervised_learning_techniques.md b/tutorials/module_5/supervised_learning_techniques.md
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+Lecture 4: Supervised Learning Techniques
+
+- Definition: supervised learning = labeled data
+- Regression tasks
+- Classification tasks
+- Classical ML models
+	- linear & logistic regression
+	- decision trees
+	- k-NN
+- Engineering examples
+	- Predicting tool wear
+	- classifying vibration patterns
+
diff --git a/tutorials/module_5/unsupervised_learning_clustering_and_dimensionality_reduction.md b/tutorials/module_5/unsupervised_learning_clustering_and_dimensionality_reduction.md
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+Lecture 6: Unsupervised learning: Clustering & Dimensionality Reduciton
+
+- Definition: 
+- k-means and hierachical clsutering
+- PCA for dimensionality reduction
+- Applications:
+	- Grouping failure modes
+	- anomaly detection in vibration data
+	- clustering material micro structure patterns
+