ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING LABORATORY
Course Code:18CSL76
CIE Marks:40
Number of Contact Hours/Week:0:0:2
SEE Marks:60
Total Number of Lab Contact Hours:36
Exam Hours:03
Credits – 2
Course Learning Objectives: This course (18CSL76) will enable students to:
- Implement and evaluate AI and ML algorithms in and Python programming language.
- Descriptions (if any):Installation procedure of the required software must be demonstrated, carried out in groups and documented in the journal.
Programs List:
1. Implement A* Search algorithm.2. Implement AO* Search algorithm.
3. 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.
4. 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 toclassify a new sample.
5. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
6. 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
7. 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.
8. 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.
9. Implement the non-parametric Locally Weighted Regressionalgorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs
Laboratory Outcomes: The student should be able to:
- Implement and demonstrate AI and ML algorithms.
- Evaluate different algorithms.
Conduct of Practical Examination:
Experiment distribution- For laboratories having only one part: Students are allowed to pick one experiment from the lot with equal opportunity.
- For laboratories having PART A and PART B: Students are allowed to pick one experiment from PART A and one experiment from PART B, with equal opportunity.
- Change of experiment is allowed only once and marks allotted for procedure to be made zero of the changed part only.
- Marks Distribution (Courseed to change in accoradance with university regulations)
- q) For laboratories having only one part – Procedure + Execution + Viva-Voce: 15+70+15 = 100 Marks
- r) For laboratories having PART A and PART B
- i. Part A – Procedure + Execution + Viva = 6 + 28 + 6 = 40 Marks
- ii. Part B – Procedure + Execution + Viva = 9 + 42 + 9 = 60 Marks
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