| 21-March-2022 |
1.5 hours |
introduction to machine learning and its applications |
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1.5 hours |
supervise, unsupervised and reinforcement learning |
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| 28-March-2022 |
1.5 hours |
Linear Regression with One Variable |
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning. |
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1.5 hours |
Linear Regression with Multiple Variables |
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning. |
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| 4-April-2022 |
1.5 hours |
Logistic Regression and its application |
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification. |
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1.5 hours |
Regularization and its description |
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data. |
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| 11-April-2022 |
1.5 hours |
Neural Networks: Representation |
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks. |
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1.5 hours |
ANN and its applications |
ANN is classification algorithm |
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| 18-April-2022 |
1.5 hours |
Perceptron's ,Multilayer Networks and Back Propagation Algorithms |
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1.5 hours |
Advanced Topics , Genetic Algorithms, Hypothesis Space Search,Genetic Programming , Models of Evolution and Learning |
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| 25-April-2022 |
1.5 hours |
Advice for Applying Machine Learning |
Aplying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models. |
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1.5 hours |
Machine Learning System Design |
To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data. |
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| 2-May-2022 |
1.5 hours |
Support Vector Machines |
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1.5 hours |
Support Vector Machines and its variants |
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| 9-May-2022 |
1 Hour |
Mid Term |
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| 16-May-2022 |
1.5 hours |
Support Vector Machines and its application |
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1.5 hours |
Random Forest classifier |
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| 23-May-2022 |
1.5 hours |
Naïve Bayes Classifier |
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1.5 hours |
BayesianBelief Network – EM Algorithm – Probability Learning – Sample Complexity |
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| 30-May-2022 |
1.5 hours |
K- Nearest Neighbour Learning |
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1.5 hours |
Locally weighted Regression – Radial Bases Functions – Case Based Learning. |
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| 6-June-2022 |
1.5 hours |
Unsupervised Learning |
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1.5 hours |
Dimensionality Reduction |
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| 13-June-2022 |
1.5 hours |
Anomaly Detection |
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1.5 hours |
Recommender Systems |
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| 20-June-2022 |
1.5 hours |
Large Scale Machine Learning |
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1.5 hours |
Application of AML |
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| 27-June-2022 |
1.5 hours |
earning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules |
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1.5 hours |
Induction on Inverted Deduction – Inverting Resolution – Analytical Learning |
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| 4-July-2022 |
1.5 hours |
Explanation Base Learning – FOCL Algorithm |
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1.5 hours |
Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning. “Current Streams of Thought |
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| 11-July-2022 |
2 Hour |
Final Term |
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