**Table Of Contents**

Discover the **algorithms** and **techniques** employed for pattern recognition, data analysis, and predictive modelling by exploring the **AKTU Btech syllabus** on **Machine Learning Techniques**. Develop clever data-driven systems to their full potential.

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**UNIT-1: INTRODUCTION**

- Learning, Types of Learning,
- Well defined learning problems,
- Designing a Learning System,
- History of ML,
- Introduction of Machine Learning Approaches – (Artificial Neural Network, Clustering,
- Reinforcement Learning,
- Decision Tree Learning,
- Bayesian networks,
- Support Vector Machine, Genetic Algorithm),
- Issues in Machine Learning and Data Science Vs Machine Learning.

**UNIT-2: REGRESSION & BAYESIAN LEARNING**

- REGRESSION: Linear Regression and Logistic Regression.

- BAYESIAN LEARNING – Bayes theorem,
- Concept learning, Bayes Optimal Classifier,
- Naive Bayes classifier,
- Bayesian belief networks,
- EM algorithm. SUPPORT VECTOR MACHINE: Introduction,
- Types of support vector kernel- (Linear kernel, polynomial kernel,and Gaussian kernel),
- Hyperplane (Decision surface),
- Properties of SVM, and Issues in SVM.

**UNIT-3: DECISION TREE LEARNING**

- DECISION TREE LEARNING-Decision tree learning algorithm,
- Inductive bias,
- Inductive inference with decision trees,
- Entropy and information theory,
- Information gain,
- ID-3 Algorithm,
- Issues in Decision tree learning.
- INSTANCE-BASED LEARNING – k – Nearest Neighbour Learning,
- Locally Weighted Regression,
- Radial basis function networks,
- Case-based learning.

**UNIT-4: ARTIFICIAL NEURAL NETWORKS**

- ARTIFICIAL NEURAL NETWORKS – Perceptron’s, Multilayer perceptron, Gradient descent & the Delta rule,
- Multilayer networks,
- Derivation of Backpropagation Algorithm,
- Generalization,
- Unsupervised Learning – SOM Algorithm and its variant;
- DEEP LEARNING -Introduction, concept of convolutional neural network,
- Types of layers -(Convolutional Layers, Activation function, pooling, fully connected),
- Concept of Convolution (1D and 2D) layers,
- Training of network,
- Case study of CNN for eg on Diabetic Retinopathy,
- Building a smart speaker,
- Self-deriving car etc.

**UNIT-5: REINFORCEMENT LEARNING**

- REINFORCEMENT LEARNING-Introduction to Reinforcement Learning,
- Learning Task,
- Example of Reinforcement Learning in Practice,
- Learning Models for Reinforcement-(Markov Decision process, Q Learning – Q Learning function, Q Learning Algorithm ),
- Application of Reinforcement Learning,
- Introduction to Deep Q Learning.

- GENETIC ALGORITHMS: Introduction,
- Components,
- GA cycle of reproduction,
- Crossover, Mutation,
- Genetic Programming,
- Models of Evolution and Learning,
- Applications.

**Important Question with solutions | AKTU Quantums | Syllabus | Short Questions**

**Machine Learning Techniques** Btech Quantum PDF, Syllabus, Important Questions

**Machine Learning Techniques**Btech

Label | Link |
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Subject Syllabus | Syllabus |

Short Questions | Short-question |

Question paper – 2021-22 | 2021-22 |

**Machine Learning Techniques** Quantum PDF | AKTU Quantum PDF:

Quantum Series | Links |

Quantum -2022-23 | 2022-23 |

## AKTU Important Links | Btech Syllabus

Link Name | Links |
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Btech AKTU Circulars | Links |

Btech AKTU Syllabus | Links |

Btech AKTU Student Dashboard | Student Dashboard |

AKTU RESULT (One VIew) | Student Result |

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