ML CA4 Analysis

Questions

1. Bayesian models

2. Graphical models

3. Distributed ML

4. Neural networks

5. In distributed machine learning, what is the primary benefit of using multiple machines for training?

   • Option A: It reduces model complexity.
   • Option B: It speeds up the training process by distributing the workload.
   • Option C: It reduces memory requirements of the model.
   • Option D: It eliminates the need for data preprocessing.

6. When selecting a model for high-dimensional, sparse data, which combination of model and regularization would likely yield the best results, and why?

   • Option A: Neural network with L1 and L2 regularization, to account for all potential non-linear relationships in sparse data
   • Option B: Linear regression with L2 regularization, as it minimizes variance in sparse datasets
   • Option C: Decision tree with no regularization, as it can handle high-dimensional data without adjustments
   • Option D: Logistic regression with L1 regularization, to promote sparsity by eliminating irrelevant features

7. Which of the following is a recent trend in machine learning to improve model interpretability? Explainable AI (XAI)

8. Which metric would be most appropriate to evaluate a classification model on a highly imbalanced dataset? F1 Score

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Analysis

Mapping of Questions to Syllabus Units

Question/Topic #	Key Concept Tested	Mapped Syllabus Unit
1	Bayesian models	Unit 5: Introduction to Bayesian Learning and Inference
2	Graphical models	Unit 5: Inference in Graphical Models
3	Distributed ML	Unit 5: Scalable Machine Learning (Online and Distributed Learning)
4	Neural networks	Unit 4: Deep Learning and Feature Representation Learning
5	Benefit of Distributed ML	Unit 5: Scalable Machine Learning (Online and Distributed Learning)
6	Sparse Modeling & Regularization	Unit 4: Sparse Modeling and Estimation
7	Recent Trends (Explainable AI)	Unit 6: Recent trends in various learning techniques
8	Model Evaluation Metrics (F1 Score)	Unit 3: Evaluating Machine Learning algorithms and Model Selection

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Concise Analysis

The provided list is a mix of high-level topics and specific questions, which suggests the assessment covers a broad range of advanced concepts.

Distribution Breakdown:

• Unit 1 (Supervised Learning): 0 items
• Unit 2 (Unsupervised Learning): 0 items
• Unit 3 (Evaluation & Model Selection): 1 item
• Unit 4 (Sparse Modeling & Deep Learning): 2 items
• Unit 5 (Scalable ML & Advanced Topics): 4 items
• Unit 6 (Recent Trends): 1 item

Key Observations:

1. Strong Focus on Advanced Topics: The majority of the items (7 out of 8) are from the latter half of the syllabus (Units 4, 5, and 6). There is a clear emphasis on modern and advanced concepts like Distributed ML, Bayesian and Graphical Models, Deep Learning, and Sparse Modeling.
2. Absence of Foundational Algorithms: The fundamental supervised and unsupervised learning algorithms detailed in Unit 1 (e.g., Linear Regression, KNN, Decision Trees) and Unit 2 (e.g., K-means, PCA) are completely missing from this sample.
3. Emphasis on Practical Considerations: The questions on evaluation metrics for imbalanced data (F1 Score) and model selection for sparse data (L1 regularization) indicate that the assessment tests practical, real-world model building considerations, not just theoretical knowledge of algorithms.

Conclusion:

Based on this sample, the assessment is heavily geared towards advanced and modern machine learning concepts rather than foundational algorithms. It tests students’ understanding of how to scale machine learning (Distributed ML), handle complex data types (Sparse Modeling), and apply modern techniques (Deep Learning, Bayesian methods). The basic algorithms from the initial units seem to be assumed knowledge rather than a direct focus of the evaluation.