| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | ||
| K3 | K4 | K5 | K5 | K6 | - | - | - | - | - | - | - | ||
| CO1 | K2 | 2 | 2 | ||||||||||
| CO2 | K2 | 2 | 2 | ||||||||||
| CO3 | K3 | 3 | 2 | 2 | |||||||||
| CO4 | K3 | 3 | 2 | 2 | |||||||||
| CO5 | K3 | 3 | 2 | 2 | |||||||||
| Score | 13 | 10 | 6 | ||||||||||
| Course Mapping | 3 | 2 | 2 |
{{{credits}}}
| L | T | P | C |
| 2 | 0 | 2 | 3 |
- To understand the need and types of machine learning techniques for various problems
- To study the various supervised learning algorithms in machine learning
- To choose appropriate machine learning algorithms to solve realistic problems.
{{{unit}}}
| UNIT I | MACHINE LEARNING LANDSCAPE | 5 |
Machine learning and it’s use; Types of machine learning; Challenges of machine learning; Testing and validating.
{{{unit}}}
| UNIT II | DEVELOPING A MACHINE LEARNING APPLICATION | 6 |
Working with real data – Look at the big picture – Get the data – Discover and visualize the data – Data preparation – Select and train model.
{{{unit}}}
| UNIT III | CLASSIFICATION AND REGRESSION | 8 |
Classification: Training a binary classifier – Performance measures – Multiclass classification; Regression: Linear regression – Gradient descent – Logistic Regression; Support Vector Machines: Linear SVM classification – Nonlinear SVM classification.
{{{unit}}}
| UNIT IV | TREE MODELS AND ENSEMBLE LEARNING | 6 |
Decision Trees: Training and visualizing trees – Making predictions – Estimating class probabilities – CART training algorithm – Regularization of hyperparameters; Ensemble learning: Voting classifiers – Bagging – Random forests – Boosting.
{{{unit}}}
| UNIT V | ARTIFICIAL NEURAL NETWORKS | 5 |
From Biological to Artificial Neurons: Biological neurons – Logical computations with neurons – Perceptron – Multi-Layer Perceptron and backpropagation; Training a MLP network – Fine tuning neural network hyperparameters; Introduction to Deep Learning.
\hfill Theory Periods: 30
- Data analysis
- Machine learning application for house price prediction
- Classification of Iris dataset using multiclass classification
- Loan amount prediction using linear regression
- E-mail spam detection using support vector machine
- Predicting Diabetes using decision tree
- Handwritten character recognition using neural networks.
\hfill Practical Periods: 30
\hfill Total Periods: 60
After the completion of this course, students will be able to:
- Explain the basic concepts and types of machine learning (K2)
- Explain the various steps in developing a machine learning application (K2)
- Apply various algorithms for classification and regression tasks (K3)
- Apply tree and ensemble models for various problems (K3)
- Apply the neural network algorithm for real world problems (K3).
- Aurelien Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, O’Reilly Media, 2017.
- Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, 2nd Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
- Jason Bell, “Machine learning – Hands on for Developers and Technical Professionals”, 1st Edition, Wiley, 2014.
- Richert, Willi, “Building Machine Learning Systems with Python”, Packt Publishing Ltd, 2013.
- Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (India) Private Limited, 2013.
- Andreas C. Muller, Sarah Guido, “Introduction to Machine Learning with Python”, O’Reilly Media, 2016.