Machine Learning (BTCS 618-18)

Course Details

Credits: 3

Hours per Week: L:3 T:0 P:0

Detailed Syllabus

UNIT 1: Introduction

Well-Posed learning problems, Basic concepts, Designing a learning system, Issues in machine learning. Types of machine learning: Learning associations, Supervised learning, Unsupervised learning and Reinforcement learning.

[4hrs] (CO 1)

UNIT 2: Data Pre-processing

Need of Data Pre-processing, Data Pre-processing Methods: Data Cleaning, Data Integration, Data Transformation, Data Reduction; Feature Scaling (Normalization and Standardization), Splitting dataset into Training and Testing set.

[4hrs] (CO 2)

UNIT 3: Regression

Need and Applications of Regression, Simple Linear Regression, Multiple Linear Regression and Polynomial Regression, Evaluating Regression Models Performance (RMSE, Mean Absolute Error, Correlation, RSquare, Accuracy with acceptable error, scatter plot, etc.)

[6hrs] (CO 3)

UNIT 4: Classification and Clustering

Need and Applications of Classification, Logistic Regression, Decision tree, Tree induction algorithm – split algorithm based on information theory, split algorithm based on Gini index; Random forest classification, Naïve Bayes algorithm; K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Evaluating Classification Models Performance (Sensitivity, Specificity, Precision, Recall, etc.). Clustering: Need and Applications of Clustering, Partitioned methods, Hierarchical methods, Density-based methods.

[12hrs] (CO 4)

UNIT 5: Advanced Topics

Association Rules Learning: Need and Application of Association Rules Learning, Basic concepts of Association Rule Mining, Naïve algorithm, Apriori algorithm. Artificial Neural Network: Need and Application of Artificial Neural Network, Neural network representation and working, Activation Functions. Genetic Algorithms: Basic concepts, Gene Representation and Fitness Function, Selection, Recombination, Mutation and Elitism.

[14hrs] (CO 5)

Course Outcomes

After completing this course, students will be able to:

  1. Analyse methods and theories in the field of machine learning
  2. Analyse and extract features of complex datasets
  3. Deploy techniques to comment for the Regression
  4. Comprehend and apply different classification and clustering techniques
  5. Understand the concept of Neural Networks and Genetic Algorithm

Text Books

  1. Mitchell M., T., Machine Learning, McGraw Hill (1997) 1st Edition.
  2. Alpaydin E., Introduction to Machine Learning, MIT Press (2014) 3rd Edition.
  3. Vijayvargia Abhishek, Machine Learning with Python, BPB Publication (2018)

Reference Books

  1. Bishop M., C., Pattern Recognition and Machine Learning, Springer-Verlag (2011) 2nd Edition.
  2. Michie D., Spiegelhalter J. D., Taylor C. C., Campbell, J., Machine Learning, Neural and Statistical Classification. Overseas Press (1994).

Machine Learning Lab (BTCS 619-18)

Course Details

Credits: 1

Hours per Week: L:0 T:0 P:2

List of Experiments

  1. Implement data pre-processing techniques
  2. Deploy Simple Linear Regression
  3. Simulate Multiple Linear Regression
  4. Implement Decision Tree
  5. Deploy Random forest classification
  6. Simulate Naïve Bayes algorithm
  7. Implement K-Nearest Neighbors (K-NN) and k-Means
  8. Deploy Support Vector Machine and Apriori algorithm
  9. Simulate Artificial Neural Network
  10. Implement the Genetic Algorithm code

Suggested Tools

Python/R/MATLAB