ML System Optimisation
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ML System Optimisation studies how to make machine learning workloads faster, more scalable, more memory-efficient, and suitable for different hardware platforms.
The subject connects machine learning algorithms with the systems that train and deploy them: multi-core CPUs, GPUs, distributed clusters, cloud platforms, edge devices, and embedded systems.
ML system optimisation = model quality + computational efficiency + hardware awareness + scalability
The learning path begins with performance measurement and parallel computing, progresses through distributed machine learning and scale-out platforms, and concludes with model compression and resource-constrained deployment.
Unsupervised Learning
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Unsupervised Learning is used when we have input data but no target labels.
The model is not told the correct answer. Instead, it tries to discover hidden structure in the data.
- K-means Clustering and variants
- Review of EM algorithm
- GMM based Soft Clustering
- Applications
Supervised vs Unsupervised Learning
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| Aspect | Supervised Learning | Unsupervised Learning |
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| Data contains target label? | Yes | No |
| Learns from | Input-output pairs | Input features only |
| Main goal | Predict output | Discover structure |
| Example task | Classification, regression | Clustering |
| Example algorithm | Logistic regression, decision tree | K-means, GMM |
- Works on unlabelled raw data.
- The algorithm discovers hidden patterns without prior knowledge of outcomes.
- Requires no human intervention during training.
- Does not make direct predictions — it groups or organises data instead.
- Carries a higher risk because there’s no ground truth to verify results.
- Common techniques include Clustering, Association, and Dimensionality Reduction.
The most common example is clustering, where similar records are grouped together.