Machine Learning
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Follow on Instagram for UpdatesMachine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Machine Learning (subject code BE05000211) as prescribed for CS-AI/ML · Semester 5 divides the course into 6 units, covering 37 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 (BE05000211)Unit 1 Introduction
- What is Learning
- Machine Learning
- Comparison of machine learning with traditional programming
- AI vs ML vs DL, Machine learning lifecycle
- Types of machine learning: Supervised
- Unsupervised and Reinforcement learning
- Types of problems: Regression
- Classification and Clustering
- Dataset: Training
- Bias-Variance Tradeoff
Unit 2 Supervised Learning - Regression
- Simple Linear regression
- Multiple linear regression
- Gradient Descent Algorithm
- Polynomial Regression
- Cost function
Unit 3 Supervised Learning – Classification
- Logistic Regression
- Decision Tree
- Support Vector machine
- Naïve bayes
- Dimensionality Reduction - PCA
Unit 4 Ensemble Learning and Model Evaluation
- Ensemble Methods: Bagging (Random Forest)
- Boosting (XGBoost)
- Model Evaluation and Validation: Confusion Matrix
- ROC Curve
- Cross-validation techniques: k-fold
- stratified k-fold
Unit 5 Unsupervised Learning
- Introduction of Unsupervised Learning
- Application of unsupervised learning
- Clustering algorithms - Partitioning Methods (k-means and k-Medoids)
- Hierarchical Methods (Agglomerative
- Density-based methods (DBSCAN)
Unit 6 Advanced Topics
- Introduction to Neural Networks
- overview of Reinforcement Learning
- Components of Reinforcement learning
- ML Tools: Numpy
- Scikit Learn
- Case Studies of Machine Learning Applications
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