Introduction to Machine Learning

Course Syllabus

Name of the Course: Introduction to Machine Learning
LTP structure of the course: 2-1-1
Objective of the course: This course gives an introduction to machine learning. It is about unified understanding of the models and algorithms used in machine learning.
Outcome of the course: Students will be able to understand basic concept and they will be able to successfully apply it on real data set.
Course Plan:

Component	Unit	Topics for Coverage
Component 1	Unit 1	Decision Trees and K-Nearest-Neighbors, Bias- Variance decomposition, Linear Regression, Perceptron, Logistic Regression, Support Vector Machines (SVM), Kernels and nonlinear SVMs.
Component 1	Unit 2	Model Selection, Feature Selection, Ensemble Methods, Gaussian Mixture Models. Hierarchical and Flat Clustering,
Component 2	Unit 3	Linear Dimensionality Reduction, Matrix Factorization, Nonlinear Dimensionality Reduction and Manifold Learning,
Component 2	Unit 4	Artificial Neural Network (Forward/Back propagation);

Text Book: Christopher Bishop, “Pattern recognition and machine learning”, Springer, 2007.Richard
References:
- Duda, Peter Hart, David Stork, “Pattern Classification”, Wiley; Second edition
- Tom Mitchell, “Machine Learning”.
- Hal Daumé III, A Course in Machine Learning (http://ciml.info), 2015
- Kevin Murphy, “Machine learning: a probabilistic perspective”, MIT Press, 2012.