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AKTU Machine Learning Lab (BCAI651) Syllabus PDF – B.Tech 3rd Year
Machine Learning Lab BCAI651
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Official AKTU syllabus of Machine Learning Lab (BCAI651) for B.Tech 3rd Year – 1 credits, L-T-P 0-0-2. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Machine Learning Lab
Download PDFBCAI651 · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of course, the student will be able to
| CO 1 | Understand complexity of Machine Learning algorithms and their limitations | K , K 5 6 |
| CO 2 | Understand modern notions in data analysis-oriented computing | K , K 5 6 |
| CO 3 | Be capable of performing experiments in Machine Learning using real-world data | K , K 5 6 |
| CO 4 | Be capable of confidently applying common Machine Learning algorithms in practice and implementing their own | K , K 5 6 |
Course Content
Implementation of following machine learning algorithms in various projects using Python:
List of Experiments
- Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.
- For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
- Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
- Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
- Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
- Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.
- Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Java/Python ML library classes/API.
- Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.
- Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.
- Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs. Note: The Instructor may add/delete/modify/tune experiments.
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