| Course Name | Introduction to Machine Learning |
| Course Code | 25CSA211 |
| Program | B. Sc. in Physics, Mathematics & Computer Science (with Minor in Artificial Intelligence and Data Science) |
| Semester | 4 |
| Credits | 4 |
| Campus | Mysuru |
Introduction to ML, Goals and applications of machine learning. Aspects of developing a learning system: training and testing data. Types of learning: Supervised, Unsupervised, and Reinforcement. Linear regression, variance, bias, gradient descent, R2, Ridge and Lasso regression.
Logistic regression, decision boundary, classification parameters: Accuracy, precision, recall, F-measure, RoC curve. Bayesian learning: Probability theory and Bayes rule. Naive Bayes learning algorithm. Regression Decision trees: classification, entropy, information gain, ginni index and regression tree – random forest
Perceptron and backpropagation neural network – k-nearest neighbor rule. Support vector machine: multicategory generalizations, Kernels for learning non-linear functions. ADA Boost classifier. Feature engineering and feature selection. PCA and LDA
Unsupervised learning. Clustering: Learning from unclassified data. Clustering. Hierarchical Agglomerative Clustering. k-means partitional clustering. Expectation maximization (EM) for soft clustering. Semi-supervised learning with EM using labeled and unlabled data
Course Objectives:
Course Outcome:
| COs | Description |
| CO1 | Select and implement machine learning techniques and computing environments that are suitable for the applications under consideration. |
| CO2 | Recognize and implement various ways of selecting suitable hypothesis and model parameters for different machine learning techniques. |
| CO3 | Understand the significance of cost function and regularization for different machine learning algorithms to generalize well to new examples. |
| CO4 | Understand and apply scaling up machine learning techniques and associated computing techniques and technologies. |
| CO5 | Develop skills using recent machine learning software for solving practical problems. |
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