Indian Institute of Information Technology, Allahabad

Department of Information Technology

Course Syllabus a Template

1. Name of the Course: Introduction to Machine Learning

2. LTP structure of the course: 2-1-1

3. 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.

4. 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.

5. Course Plan:

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. |

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, |

Unit 4 | Artificial Neural Network (Forward/Back propagation); |

6. Text Book: Christopher Bishop, “Pattern recognition and machine learning”, Springer, 2007.Richard

7. 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.