Artificial Intelligence and Machine Learning
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Artificial Intelligence and Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence and Machine Learning (subject code 3161710) as prescribed for EIE · Semester 6 divides the course into 3 units, covering 41 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 (3161710)Unit 2 Gradient Descent and Linear Algebra Model representation, cost function,
- mathematics of gradient descent
- gradient descent intuition
- gradient descent for linear regression
- transpose matrix
- matrix multiplication, and matrix multiplication properties
- inverse of matrix
Unit 3 Multiple features gradient descent Multiple features, gradient descent calculations
- for multiple features
- features and polynomial regression
- normal equations
- linear regression with multiple variables. 04 15 4 1
- Classification Overview of classification: setup
- validation dataset
- over fitting
- Classification families: linear discriminative
- non-linear discriminative
- decision trees
- probabilistic (conditional and generative)
- nearest neighbor and Logistic regression. 04 10 5 Support Vector Machine Max margin motivation: low density
- high stability
- Margin geometry to primal SVM formulation for separable training data
- Dual formulation and role of alpha in a form of sparse local regression
- Inseparable data
- slack variables
- hinge loss
- upper bound on 0/1 training loss Handling non-linear regression by lifting data points to higher dimension 06 20 Subject Code: 3161710 w.e.f
- AY 2018-19 6 Dimensionality reduction Curse of dimensionality
- Principal Component Analysis
- Latent Semantic Analysis 04 10 7 Artificial Neural Network Model representation
- working of neurons
- back propagation algorithm
- adjusting parameters
- multiclass classification
- bias value
- random and Gaussian initialization of parameters
- final implementation of ANN 04 10 8 Unsupervised learning Mixture model and Expectation maximization
- K-Means Clustering
- Distance based clustering
- Density based clustering techniques 02 5 9 Ensemble learning Random forest
- Adaboost 02 5 10 Gradient Descent with large data set Stochastic gradient descent
- mini batch gradient descent
- stochastic gradient descent convergence
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