Enrollment for IIT Madras online courses on artificial intelligence, which are aimed at students, professionals and anybody interested, will soon end on the NPTEL platform of the Government of India. There are currently five courses related to artificial intelligence, machine learning and data science available on the platform that can be beneficial in gaining an understanding of the subject.
The courses will be conducted by IIT Madras professors, and are around 4 to 12 weeks long. While taking the online courses are free, participants can obtain certificates with logos of IIT Madras and NPTEL by paying Rs 1000 for each course, and taking examinations that will be conducted for every course in April 2021. Participants also get to earn course credits upon successful completion of the modules. The last date to enroll for the courses is 25 January 2021.
AI Constraint Satisfaction: This course aims at equipping learners with knowledge on problem-solving using artificial intelligence. Some of the topics that will be covered are Constraint satisfaction problems (CSP), examples, Lookback methods, Gaschnig’s backjumping, graph based backjumping, conflict directed back jumping. Combing lookahead with lookback, learning, and more. More information on the course can be found here.
Artificial Intelligence: Knowledge Representation and Reasoning: This course covers more advanced topics of artificial intelligence such as Proof Systems, Natural Deduction, Tableau Method, Resolution Method, First Order Logic (FOL), Syntax and Semantics, Unification, Forward Chaining, The Rete Algorithm, Rete example, Programming Rule-Based Systems, and other similar topics. More information on the course can be found here.
Introduction to Machine Learning: This course aims at introducing learners to some of the basic concepts of machine learning from a mathematically well-motivated perspective. Some of the concepts that will be covered are Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component, Regression, Partial Least squares, and so on. More information on the course can be found here.
Data Science for Engineers: Those who wish to take the course will be provided with ten hours of pre-course material. The course will cover various topics such as Course philosophy and introduction to R, Linear algebra for data science and algebraic view – vectors, matrices, the product of matrix and vector, rank, null space, solution of over-determined; set of equations and pseudo-inverse; Geometric view – vectors, distance, projections, Eigenvalue decomposition, typology of data science problems and a solution framework, etc. More information on the course can be found here.
Python for Data Science: The course aims at equipping learners with the ability to be able to use python programming for solving data science problems. Some of the topics that will be covered are introduction Spyder, setting working director, creating and saving a script file, file execution, clearing console, removing variables from environment, clearing environment, commenting script files, and other related topics. More information on the course can be found here.
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