| Course Name | Machine Learning and Artificial Intelligence |
| Course Code | 19CCE213 |
| Program | B. Tech. in Computer and Communication Engineering |
| Semester | Four |
| Year Taught | 2019 |
Measuring the central tendency – measuring the dispersion of data – graphic displays of basic descriptive data Summaries – Missing values – noisy data- data cleaning as a process – Data integration – data transformation – Data cube aggregation – attribute subset selection – dimensionality reduction.
Cluster Analysis using k–Means – k–Medoids – single linkage – complete linkage – UPGMA and expectation Maximization – Assessing clustering tendency – determining the number of clusters – measuring clustering quality k– nearest neighbor – Bayes – decision tree and Support Vector Machines (SVM) classifiers – Classifier accuracy Measures – evaluating the accuracy of a Classifier.
Efficient and Scalable Frequent Itemset Mining Methods- Mining Various Kinds of Association Rules- From Association Mining to Correlation Analysis- Constraint-Based Association Mining.
Experiments on machine learning and artificial intelligence algorithms using Matlab / Python.
Evaluation Pattern
| Assessment | Internal | External |
| Periodical 1 | 15 | – |
| Periodical 2 | 15 | – |
| *Continuous Assessment (CAT) | 20 | – |
| *Continuous Assessment (CAL) | 30 | |
| End Semester | – | 35 |
| *CA – Can be Quizzes, Assignment, Projects, and Reports. | ||
Objectives
Course Outcomes
CO – PO Mapping
| PO/PSO/CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO1 | 3 | 3 | – | – | – | – | – | – | – | – | – | – | – | – |
| CO2 | – | 3 | 2 | – | 2 | – | – | – | – | – | – | – | 3 | – |
| CO3 | – | 2 | 3 | 2 | 2 | – | – | – | – | – | – | – | 3 | – |
| CO4 | – | 2 | 3 | 2 | 2 | – | – | – | – | – | – | – | 3 | – |
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