MC4311 Machine Learning Laboratory Syllabus:

MC4311 Machine Learning Laboratory Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

 To understand about data cleaning and data preprocessing
 To familiarize with the Supervised Learning algorithms and implement them in practical situations.
 To familiarize with unsupervised Learning algorithms and carry on the implementation part.
 To involve the students to practice ML algorithms and techniques.
 Learn to use algorithms for real time data sets.

LIST OF EXPERIMENTS :

1. Demonstrate how do you structure data in Machine Learning
2. Implement data preprocessing techniques on real time dataset
3. Implement Feature subset selection techniques
4. Demonstrate how will you measure the performance of a machine learning model
5. Write a program to implement the naïve Bayesian classifier for a sample training data set. Compute the accuracy of the classifier, considering few test data sets.
6. Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using the standard Heart Disease Data Set.
7. Apply EM algorithm to cluster a set of data stored in a .CSV file.
8. Write a program to implement k-Nearest Neighbor algorithm to classify the data set.
9. Apply the technique of pruning for a noisy data monk2 data, and derive the decision tree from this data. Analyze the results by comparing the structure of pruned and unpruned tree.
10. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets
11. Implement Support Vector Classification for linear kernels.
12. Implement Logistic Regression to classify problems such as spam detection. Diabetes predictions and so on.

TOTAL: 60 PERIODS

LAB REQUIREMENTS:

Python or any ML tools like R

COURSE OUTCOMES:

On completion of the laboratory course, the student should be able to
CO1: apply data preprocessing technique and explore the structure of data to prepare for predictive modeling
CO2: understand how to select and train a model and measure the performance.
CO3: apply feature selection techniques in Machine Learning
CO4: construct Bayesian Network for appropriate problem
CO5: learn about parametric and non-parametric machine Learning algorithms and implement to practical situations