Introduction of Machine learning
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Introduction of Machine learning Syllabus — Unit-wise Content
The official GTU curriculum for Introduction of Machine learning (subject code 3171114) as prescribed for ECE · Semester 7 divides the course into 8 units, covering 33 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 (3171114)Unit 1 Introduction to Machine Learning
- Different Types of Learning
- Hypothesis Space
- Inductive Bias
- Evaluation and Cross Validation
Unit 2 Basic Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Learning Decision Trees
- K-nearest Neighbour
- Collaborative Filtering
Unit 3 Dimensionality Reduction
- Feature Selection
- Feature Extraction
Unit 4 Bayesian Concept of Learning
- Bayesian Learning
- Naïve Bayes
- Bayesian Network
- Exercise on Naïve Bayes
Unit 5 Logistic Regression and Support Vector Machine
- Logistic Regression
- Introduction to Support Vector Machine
- The Dual Formation
- Maximum Margin with Noise
- Nonlinear SVM and Kernel Function
- SVM: Solution to the Dual Problem
Unit 6 Basics of Neural Network
- Introduction to neural network
- Multilayer Neural Network
- Neural Network and Backpropagation Algorithm
- Deep Neural Network
Unit 7 Computation and Ensemble Learning
- Introduction to Computation Learning
- Sample Complexity: Finite Hypothesis Space
- VC Dimension
- Introduction to Ensembles
- Bagging and Boosting
Unit 8 Basic Concepts of Clustering
- Introduction to Clustering
- K-means Clustering
- Agglomerative Hierarchical Clustering 06 Subject Code: 3171114 w.e.f
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