Mathematical Foundation for Computing
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Follow on Instagram for UpdatesMathematical Foundation for Computing Syllabus — Unit-wise Content
The official GTU curriculum for Mathematical Foundation for Computing (subject code ME01095051) as prescribed for AIAD · Semester 1 divides the course into 6 units, covering 31 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME01095051)Unit 1 Probability mass, density, and cumulative distribution functions,
- Parametric families of distributions
- Expected value
- conditional expectation
- Applications of the univariate and multivariate Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095051 Subject Name: Mathematical Foundation for Computing Central Limit Theorem
- Probabilistic inequalities
- Markov chains
Unit 2 Random samples, sampling distributions of estimators,
- Maximum likelihood estimation (MLE) in Binomial
- models in exponential family
Unit 3 Statistical inference, Introduction to multivariate statistical models
- regression and classification problems
- principal components analysis
- The problem of overfitting model assessment Time Series Analysis Time series forecasting models
- Model evaluation
- Applications in data science
Unit 4 Graph Theory
- Planar graphs
- graph colouring
- hamilton circuits and euler cycles
- Permutations and Combinations with and without repetition
- Specialized techniques to solve combinatorial enumeration problems
- Application of graph theory in neural networks and data science Information Theory Entropy: Understanding uncertainty and its implications in data science
- Cross-Entropy: Common loss function in classification problems
Unit 5 Convex functions and sets, Convex Optimization, Optimization
- Formulations of Data Analysis Problems
- Gradient and Sub-gradient descent for non-smooth functions e.g.: SVM, Online Gradient Descent e.g.: Stochastic Gradient Descent and its applications (NN)
- Duality and its examples
- Accelerating Gradient Descent Advanced Optimization Methods: Techniques like Adam
- RMSprop, and momentum
Unit 6 Mathematics for machine learning-
- Expectation Maximization (EM) based learning in Mixture models
- Hidden Markov Model
- Dirichlet processes (Clustering)
- Bayesian Machine Learning
- estimating decisions using posterior distributions
- Model selection: Variational Inference
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