Introduction to Machine Learning
By Prof. Balaraman Ravindran | IIT Madras
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
INTENDED AUDIENCE : This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
PREREQUISITES : We will assume that the students know programming for some of the assignments.If the students have done
introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic
topics in the first two weeks as well.
INDUSTRY SUPPORT : Any company in the data analytics/data science/big data domain would value this course.
https://swayam.gov.in/nd1_noc20_cs29/preview
COURSE LAYOUT
Week 1 : Introduction to the Machine Learning course
Week 2 : Characterization of Learning Problems
Week 3 : Forms of Representation
Week 4 : Inductive Learning based on Symbolic Representations and Weak Theories
Week 5 : Learning enabled by Prior Theories
Week 6 : Machine Learning based Artificial Neural Networks
Week 7 : Tools and Resources + Cognitive Science influences
Week 8 : Examples, demos and exam preparations
By Prof. Balaraman Ravindran | IIT Madras
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
INTENDED AUDIENCE : This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
PREREQUISITES : We will assume that the students know programming for some of the assignments.If the students have done
introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic
topics in the first two weeks as well.
INDUSTRY SUPPORT : Any company in the data analytics/data science/big data domain would value this course.
https://swayam.gov.in/nd1_noc20_cs29/preview
COURSE LAYOUT
Week 1 : Introduction to the Machine Learning course
Week 2 : Characterization of Learning Problems
Week 3 : Forms of Representation
Week 4 : Inductive Learning based on Symbolic Representations and Weak Theories
Week 5 : Learning enabled by Prior Theories
Week 6 : Machine Learning based Artificial Neural Networks
Week 7 : Tools and Resources + Cognitive Science influences
Week 8 : Examples, demos and exam preparations
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