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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:


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:

  1. Duda, Peter Hart, David Stork, “Pattern Classification”, Wiley; Second edition 
  2. Tom Mitchell, “Machine Learning”. 
  3. Hal Daumé III, A Course in Machine Learning (, 2015
  4. Kevin Murphy, “Machine learning: a probabilistic perspective”, MIT Press, 2012.