MS/PhD
Source: https://wsai.iitm.ac.in/admissions/ms-phd-admissions/ Parent: https://wsai.iitm.ac.in/
MS/PhD
For those passionate about research and innovation, our advanced degree programs offer opportunities to delve into cutting-edge research in AI and data science, guided by our expert faculty.
The School of Data Science and AI at IIT Madras offers MS/PhD programmes in a variety of areas. We welcome applicants from diverse departments with an applied interest in AI/ML, as well as students from EE/CS/AI departments who are interested in fundamental research areas in AI/ML.
Written Test Syllabus
The written test will have two parts: Theory (objective questions: MCQ, Fill in the blanks, True/False) and Python Coding (problems requiring you to write code in Basic Python).
Probability and Statistics
- Counting (permutation and combinations)
- Independent events, mutually exclusive events
- Marginal, conditional and joint probability
- Bayes Theorem
- Conditional expectation and variance
- Mean, median, mode and standard deviation
- Correlation, and covariance
- Random variables, discrete random variables and probability mass functions
- Uniform, Bernoulli, binomial distribution
- Continuous random variables and probability
- Distribution function, cumulative distribution function, Conditional PDF
- Uniform, exponential, Poisson, normal, standard normal, t-distribution
- Chi-squared distributions
- Central limit theorem
- Confidence interval
- Z-test, t-test, chi-squared test
Linear Algebra
- Vector space, subspaces
- Linear dependence and independence of vectors
- Matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix
- Quadratic forms
- Systems of linear equations and solutions
- Gaussian elimination
- Eigenvalues and eigenvectors
- Determinant, rank, nullity
- Projections
- LU decomposition, singular value decomposition
Calculus and Optimization
- Functions of a single variable
- Limit, continuity and differentiability
- Taylor series
- Maxima and minima
- Optimization involving a single variable
Python Coding
You will be given coding tasks that you need to complete and execute by writing Python scripts. You will need to know the following:
- Basic Python syntax - comments, variables, basic data types
- Operators and Control Flow - If/else, for, while, range, break, continue, pass
- Functions - How to define and use them
- Lists/Arrays, Tuples, and associated methods
Interview Topics
For those who qualify after the written test for the online interview, questions from the following additional topics may be asked:
Machine Learning
- Supervised Learning regression and classification problems
- Simple linear regression
- Multiple linear regression
- Ridge regression
- Logistic regression
- K-nearest neighbour
- Naive Bayes classifier
- Linear discriminant analysis
- Support vector machine
- Decision trees
- Bias-variance trade-off
- Cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network
- Unsupervised Learning: clustering algorithms
Artificial Intelligence
- Search: informed, uninformed, adversarial
- Logic: Propositional Logic, Predicate Logic
- Reasoning under Uncertainty Topics
- Conditional Independence Representation
- Exact Inference through Variable Elimination
- Approximate Inference through Sampling
Note for PhD Applicants: You may also be asked questions from specialized topics during the interview. You can select a topic from Deep Learning, NLP, Vision, RL, or Time-Series modeling depending on your interest and background.
Sample Question Papers
Download sample question papers to prepare for the written test. The GATE DA (Data Science and AI) model question paper is also a good reference, as our test is structured in a similar fashion.
DSAI MS/PhD Sample Question Paper
Practice questions for the written test
GATE DA Sample Question Paper
Data Science and AI model question paper
DA 2024 GATE Question Paper
Official GATE 2024 question paper