IIT Kharagpur is inviting applications for the 8-weeks free online course on Machine Learning via the NPTEL platform.Indian Institute of Technology (IIT) Kharagpur is inviting applications for Machine Learning free online courses introduced on the SWAYAM NPTEL platform. The course will start on July 26 and ends on September 17, 2021. The candidates will get the certification from NPTEL and IIT Kharagpur and can take an examination that is to be conducted on September 26, 2021, with the payment of Rs 1000. It will give two credit points to the students that can come in handy to the engineering students.
The Head of the Department of Computer Science and Engineering at IIT Kharagpur, Professor Sudheshna Sarkar will conduct the online course. He completed his B.Tech from IIT Kharagpur in 1989 followed by the MS from the University of California, Berkeley. In 1995, he gained his PhD from IIT Kharagpur and has an interest in Natural Language Processing, Machine Learning, Text Mining, and Data.
The free online course on Machine Learning can be taken by professionals and students with proficiency or interest in Artificial Intelligence, Computer Science and Engineering, Programming, Data Science, and Robotics. It can serve as an elective course for postgraduate, undergraduate, MSc, BE, ME, MS< and PhD students.
Machine Learning syllabus
- Basics of computational learning theory and issues related to machine learning algorithms and applications
- Solving hands-on-problems with programming in Python and tutorial sessions
- Linear regression, overfitting, and Decision trees
- Introduction: Basics, hypothesis space, type of learning, evaluation, inductive bias, cross-validation
- Probability and Bayes learning
- Instance-based learning, collaborative filtering-based recommendation, and feature reduction
- Neural network: multilayer network, perceptron, introduction to the deep neural network, and backpropagation
- Logistic regression, kernel function, support vector machine, and kernel SVM
- Clustering: Gaussian mixture, adaptive hierarchical clustering, and k-mean model
- Computational learning theory, sample complexity, PAC learning model, Ensemble learning, and VC Dimension
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