Diploma Level Course

Machine Learning Techniques

To introduce the main methods and models used in machine learning problems of regression, classification and clustering. To study the properties of these models and methods and learn about their suitability for different problems.

Taught by Ashish Tendulkar

Code BSCS2007

Credits 4 Credits

Type Data Science

Prerequisites None

Co-requisites

BSCS2004

Core Competencies

What You'll Learn

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Demonstrating In depth understanding of machine learning algorithms - model, objective or loss function, optimization algorithm and evaluation criteria.
Tweaking machine learning algorithms based on the outcome of experiments - what steps to take in case of underfitting and overfitting.
Being able to choose among multiple algorithms for a given task.
Developing an understanding of unsupervised learning techniques.

12-Week Roadmap

Course Structure & Syllabus

For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1

Introduction to machine learning; Supervised vs unsupervised, batch vs online, instance-based vs model-based; Problems - regression, classification, clustering; Challenges

WEEK 2

Models of regression; Linear regression - least squares; Polynomial regression - learning curves; Regularized linear models - Ridge, LASSO

WEEK 3

Models of regression; Linear regression - least squares; Polynomial regression - learning curves; Regularized linear models - Ridge, LASSO

WEEK 4

Models of classification; Discriminant functions and decision boundaries - two classes, multiple classes, least squares, perceptron; Probabilistic generative and discriminative models - ML, Naive Bayes, exponential family, logistic regression

Reading List

Prescribed Books & References

Pattern Classification by David G. Stork, Peter E. Hart, and Richard O. Duda
Pattern Recognition and Machine Learning by Christopher M. Bishop
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Faculty & Experts

About the Instructors

Ashish Tendulkar

Research Software Engineer , Google AI , Google

Dr. Ashish Tendulkar is a researcher with Google Research Bangalore. He holds Masters and PhD from IIT Bombay. Before his current position, he was an Assistant Professor at IIT Madras and Head of data sciences at Persistent Systems Pune. Ashish is passionate about teaching ML and writing AI related contents in Indian languages.

Other Courses by Instructor:

BSCS2008 • Machine Learning Practice

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