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lw3/README.md

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+# Classification I
+
+## K-nearest neighbor classification
+
+The objective is to study the use of the method k-nn and
+
+1. Select two datasets with labels.
+2. Run k-nn on these two datasets. Calculate the classification error (by comparing the class
+   labels obtained with the prediction and the original labels of test data).
+3. Vary the value of k, comment on the results
+4. Try to normalize the input dataset, is the performance better?
+5. Apply PCA and SVD on the dataset, then what is the performance of k-nn on the new
+   projected data? Justify the answer.
+6. Propose an approach to improve the performance of k-nn with the use of k-cross validation.
+7. Apply leave-one-out and calculate the error of classification.
+
+## SVM classifier
+
+Suppose that we use the SVM classifier from sklearn in Python.
+
+- Select two datasets with labels.
+- Analyze the dataset. How is the data distribution? linearly separable or non-linearly seperable?
+- Set up the SVM parameters suitable to the selected datasets.
+- What is the performance of SVMs?
+- How can SVM handle multi-class datasets?