CSPC-513 Introduction to Machine Learning
Teaching Scheme			Credit	Marks Distribution			Duration of End Semester Examination
L	T	P		Internal Assessment	End Semester Examination	Total
3	1	0	4	Maximum Marks: 40	Maximum Marks: 60	100	3 Hours
				Minimum Marks: 16	Minimum Marks: 24	40

Unit-I

Introduction: Machine Learning Paradigms: Introduction to machine learning, data sets, feature sets, data set division-test, train and validation sets, Cross Validation, applications of Machine Learning, process involved in machine learning, Types of machine learning.

Unit-II

Supervised Learning: Classification and Regression: K- Nearest neighbor, Linear regression, multi-linear Regression, Logistic Regression, Support Vector Machine (SVM), Decision Trees, Naïve Bayes algorithm, Random Forest Algorithm.

Unit-III

Unsupervised learning: Introduction, Types of Clustering, Hierarchical Clustering- Agglomerative clustering and divisive clustering, Partitional clustering. Clustering Algorithms: K-means clustering, mean-shift algorithm. Association rules. Dimensionality Reduction: PCA, k-nearest neighbors and discriminant analysis.

Unit-IV

Reinforcement learning: Types of reinforcement learning: positive and negative, reinforcement learning.

Algorithms models: model based and model free algorithms, on policy and off policy, Markov decision process, Q Learning, Application of reinforcement Learning.