Machine Learning Techniques (BCS055) AKTU syllabus – unit-wise topics
The complete Machine Learning Techniques syllabus for AKTU, unit by unit. Next to each topic you can see how often it has appeared in past papers, so you know where the marks are.
Unit 1: Introduction
- Learning – asked 2 times
- Types of Learning – asked 13 times
- Well-Defined Learning Problems – asked 6 times
- Designing a Learning System – asked 4 times
- History of Machine Learning – asked 1 times
- Introduction to Machine Learning Approaches – asked 4 times
- Issues in Machine Learning – asked 2 times
- Data Science versus Machine Learning – asked 2 times
Unit 2: Regression, Bayesian Learning and Support Vector Machine
- Linear Regression – asked 4 times
- Logistic Regression – asked 7 times
- Bayes Theorem – asked 2 times
- Concept Learning – asked 5 times
- Bayes Optimal Classifier – asked 1 times
- Naive Bayes Classifier – asked 6 times
- Bayesian Belief Networks – asked 2 times
- Expectation-Maximization Algorithm – asked 5 times
- Introduction to Support Vector Machine – asked 3 times
- Types of Support Vector Kernels – asked 5 times
- Hyperplane and Decision Surface – asked 3 times
- Properties of SVM – asked 2 times
- Issues in SVM – asked 1 times
Unit 3: Decision Tree Learning and Instance-Based Learning
- Decision Tree Learning Algorithm – asked 2 times
- Inductive Bias – asked 3 times
- Inductive Inference with Decision Trees – asked 1 times
- Entropy and Information Theory – asked 2 times
- Information Gain – asked 2 times
- ID-3 Algorithm – asked 4 times
- Issues in Decision Tree Learning – asked 7 times
- k-Nearest Neighbour Learning – asked 6 times
- Locally Weighted Regression – asked 4 times
- Radial Basis Function Networks – asked 3 times
- Case-Based Learning – asked 2 times
Unit 4: Artificial Neural Networks and Deep Learning
- Perceptrons – asked 3 times
- Multilayer Perceptron – asked 2 times
- Gradient Descent and the Delta Rule – asked 4 times
- Multilayer Networks – asked 1 times
- Derivation of Backpropagation Algorithm – asked 8 times
- Generalization – asked 1 times
- Unsupervised Learning: SOM Algorithm and Its Variants – asked 3 times
- Introduction to Deep Learning – asked 2 times
- Concept of Convolutional Neural Network – asked 3 times
- Types of CNN Layers – asked 6 times
- Concept of 1D and 2D Convolution Layers
- Training of Network
- CNN Case Studies – asked 1 times
Unit 5: Reinforcement Learning and Genetic Algorithms
- Introduction to Reinforcement Learning – asked 6 times
- Reinforcement Learning Task – asked 1 times
- Examples of Reinforcement Learning in Practice
- Markov Decision Process – asked 5 times
- Q Learning – asked 7 times
- Applications of Reinforcement Learning
- Introduction to Deep Q Learning – asked 2 times
- Introduction to Genetic Algorithms – asked 1 times
- Components of Genetic Algorithms – asked 3 times
- GA Cycle of Reproduction – asked 7 times
- Crossover – asked 1 times
- Mutation
- Genetic Programming – asked 1 times
- Models of Evolution and Learning
- Applications of Genetic Algorithms