Applied Machine Learning
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Applied Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Applied Machine Learning (subject code 3171617) as prescribed for Information Technology · Semester 7 divides the course into 6 units, covering 36 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 (3171617)Unit 1 Introduction to Machine Learning
- Overview of Human Learning and Machine Learning
- Types of Learning
- Applications of Machine Learning
- Tools and Technology for Machine Learning
Unit 2 Overview of Probability
- Statistical tools in Machine Learning
- Concepts of probability
- Random variables
- Discrete distributions
- Continuous distributions
- Multiple random variables
- Central limit theorem
- Sampling distributions
- Hypothesis space and inductive bias
- Evaluation and Cross Validation
- Hypothesis testing
- Monte Carlo Approximation
Unit 3 Bayesian Concept Learning
- Impotence of Bayesian methods
- Bayesian theorem
- Bayes’ theorem and concept learning
- Bayesian Belief Network
Unit 4 Classification and Regression
- Supervised Learning vs Unsupervised Learning
- Supervised Learning
- Classification Model
- Learning steps
- Classification algorithms
- Association rules
- Linear Regression
- Multivariate Regression
- Logistic Regression
Unit 5 Neural Networks-Introduction, Early Models, Perceptron
- Training & Validation
- Parameter Estimation - MLE, MAP, Bayesian Estimation
Unit 6 Foundations of neural networks and deep learning, Techniques to improve
- neural networks: Regularization and optimizations
- hyperparameter tuning and deep learning frameworks (Tensorflow and Keras.)
- Convolutional Neural Networks
- its applications
- Recurrent Neural Networks and its applications
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