Department Events
Artificial Intelligence and Machine Learning
The objective of course is to:
1. Introduce students to basic applications, concepts, and techniques of machine learning.
2. Develop skills in students to implement machine learning algorithms on real world problems and evaluate their performance.
Course | Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | CO127. Formulate a problem for data analytics and analyse data and select suitable machine learning technique for designing a model. CO128. Develop a machine learning model for unlabelled data using association rules. CO129. Design a machine learning model for data using clustering techniques. CO130 Analyse and implement different classification techniques. CO131. Analyse and compare different prediction techniques. CO132 Contribute effectively in course-specific interaction | Approach in teaching: Interactive Lectures, Group Discussion, Case Study, Demonstration
Learning activities for the students: Self-learning assignments, Exercises related with Machine Learning algorithm, presentations | Class test Semester end examination Quiz Practical Assignments Presentation |
25CBDA 311 |
Artificial Intelligence and Machine Learning (Theory) | |||
Introduction to Data Mining and machine learning: Basic Data Mining Tasks, Data Mining versus Knowledge Discovery in Databases, Applications of Machine Learning, Machine Learning vs AI, Types of Machine Learning, Metrics, Accuracy Measures: Precision, recall, F-measure, confusion matrix, cross-validation.
Unsupervised Learning: Understand the Problem by Understanding the Data, unbalanced data, Association rules, Apriori algorithm, FP tree algorithm, and their implementation in Python, Market Basket Analysis and Association Analysis.
Clustering: k-means and implementation of k-means using python, Concept of other clustering algorithms: Expectation Maximization (M) algorithm, Hierarchical clustering, and DBSCAN.
Classification & Prediction: model Construction, performance, attribute selection Issues: under, Over-fitting, cross validation, tree pruning methods, missing values, Information Gain, Gain Ratio, Gini Index, continuous classes. Classification and Regression Trees (CART) and C 5.0., implementation of decision tree in python
Classification & Prediction: Linear Regression, Multiple Linear Regression, Logistic Regression, Naïve Bayes and Support Vector Machines(SVM), Implementation of Linear Regression, Logistic Regression, Naïve Bayes and SVM in python.
1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
2. Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Second Edition,Packt>.
3. McKinney, Python for Data Analysis. O’ Reilly Publication,2017.
SUGGESTED READINGS:
1. Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018. (Latest editions of the above books are to be referred)
e RESOURCES:
1. https://www.jigsawacademy.com/blogs/business-analytics/
2. NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212
3. Python, w3scool, https://www.w3schools.com/
4. Jupiter :www.jupiter.com
5. Googlecolab: www.googlecolab.com
JOURNALS:
1. Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
2. International Journal of Machine Learning and Cybernetics, springer :https://www.springer.com/journal/13042
