Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Landscape PPTs

 

Unit I (B)

The Machine Learning Landscape

1.     Main Challenges of Machine Learning

2.     Insufficient Quantity of Training Data

3.     Nonrepresentative Training Data

4.     Poor-Quality Data

5.     Irrelevant Features

6.     Overfitting the Training Data

7.     Underfitting the Training Data

8.     Stepping Back

9.     Testing and Validating

 

Machine Learning 1: UNIT-1(B) NOTES: The Machine Learning Landscape NOTEs

                                                                                     Unit I (B)

The Machine Learning Landscape

1.     7.     Main Challenges of Machine Learning
8.     Insufficient Quantity of Training Data
9.     Nonrepresentative Training Data
10.  Poor-Quality Data
11.  Irrelevant Features
12.  Overfitting the Training Data
13.  Underfitting the Training Data
14.  Stepping Back
15.  Testing and Validating

 

Machine Learning 1 : UNIT 1 (A) PPTs: The Machine Learning Landscape PPTs

                                                    Unit I: 

                    The Machine Learning Landscape

1. What Is Machine Learning?
2.  Why Use Machine Learning?
3.  Types of Machine Learning Systems
4.  Supervised/Unsupervised Learning
5.  Batch and Online Learning
6.  Instance-Based Versus Model-Based Learning

Machine Learning1: UNIT 1 (A) NOTEs : The Machine Learning Landscape NOTEs

 Unit I

The Machine Learning Landscape

1.     What Is Machine Learning?

2.     Why Use Machine Learning?

3.     Types of Machine Learning Systems

4.     Supervised/Unsupervised Learning

5.     Batch and Online Learning

6.     Instance-Based Versus Model-Based Learning

 

 

About Machine Learning 1

 Machine Learning

• The Machine Learning Landscape
• Classification
• Support Vector Machines
• Decision Trees
• Ensemble Learning and Random Forests
• Dimensionality Reduction
• Clustering

👉YouTube Link: https://www.youtube.com/@drrambabupemula

👉 Machine Learning 1 Syllabus

Unit I:

The Machine Learning Landscape: What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning, Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating.

👉UNIT 1(A) NOTEs : The Machine Learning Landscape Notes

👉UNIT 1(A) PPTs: The Machine Learning Landscape

👉UNIT 1(B) NOTEs: The Machine Learning Landscape NOTEs

👉Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Landscape PPTs

👉Machine Learning 1: UNIT 1 The Machine Learning Landscape Questions

Unit II:

Classification: Training a Binary Classifier, Performance Measures, Measuring Accuracy UsingCross-Validation, Confusion Matrix, Precision and Recall, Precision/RecallTradeoff , The ROC Curve, Multiclass Classification, Error Analysis, Multilabel Classification, Multi Output Classification. k-NN Classifier.

👉Machine Learning 1: UNIT 2 NOTEs: Classification Notes

👉Machine Learning 1 : UNIT 2: Classification PPTs

👉Machine Learning 1: UNIT 2: Classification MCQs

👉Machine Learning 1: UNIT 2: Classification Questions

Unit III:

Support Vector Machines: Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression, Under the Hood, Decision Function and Predictions, Training Objective, Quadratic Programming, The Dual Problem, Kernelized SVM, Online SVMs.

👉Machine Learning 1: UNIT 3 (A) NOTES: Support Vector Machines NOTEs

👉Machine Learning 1: UNIT 3 (A) PPTs: Support Vector Machines PPTs

👉Machine Learning 1: UNIT 3 (B) NOTEs: Support Vector Machines NOTEs

👉Machine Learning 1: UNIT 3 (B) PPTs: Support Vector Machines PPTs

👉Machine Learning 1: UNIT 3 A & B : Support Vector Machines Questions

👉Machine Learning 1: UNIT 3 : Support Vector Machines MCQs

Unit IV:

Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexity, Gini Impurity or Entropy? Regularization Hyperparameters, Regression

👉Machine Learning 1: UNIT 4 (A) NOTEs: Decision Trees NOTEs

👉Machine Learning 1: UNIT 4 (A) PPTs: Decision Trees PPTs

👉Machine Learning 1: UNIT 4 (A): Decision Trees Questions

👉Machine Learning 1: UNIT 4 (A) : Decision Trees MCQs

Ensemble Learning and Random Forests: Voting Classifiers, Bagging and Pasting, Bagging and Pasting in Scikit-Learn, Out-of-Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extra-Trees, Feature Importance, Boosting, AdaBoost, Gradient Boosting, Stacking.

👉Machine Learning 1: UNIT 4 (B) NOTES: Ensemble Learning and Random Forests NOTES

👉Machine Learning 1: UNIT 4 (B) PPTs: Ensemble Learning and Random Forests PPTs

👉Machine Learning 1: UNIT 4 (B) : Ensemble Learning and Random Forests Questions

👉Machine Learning 1: UNIT 4 (B) : Ensemble Learning and Random Forests MCQs

Unit V:

Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, Projection, PCA.

👉Machine Learning 1: UNIT-5(A) NOTES: Dimensionality Reduction NOTES

👉Machine Learning 1: UNIT-5(A) PPTs: Dimensionality Reduction PPTs

👉Machine Learning 1: UNIT-5(A): Dimensionality Reduction Questions

Clustering: How does clustering work: finding similarities using distances, Euclidean distance and other distance metrics. k-Means Clustering: Plotting customers with their segments, normalizing features, cluster centres and interpreting the Clusters. Hierarchical Clustering.

👉Machine Learning 1: UNIT 5(B) NOTEs: Clustering NOTES

👉Machine Learning 1: UNIT 5 (B) PPTs: Clustering PPTs

👉Machine Learning 1: UNIT 5 (B) : Clustering Questions

Textbooks:

1. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.

2. Pradhan, Manaranjan, and U. Dinesh Kumar. Machine Learning using Python. Wiley, IIM Bangalore, 2019.

References: 

1.     Introduction to Machine Learning, Ethem Alpaydin 2nd Edition, MIT Press 2000

2.     Machine Learning, Tom M. Mitchell, McGraw Hill, 1997, ISBN: 0-07-042807-7. 

Machine Learning 1 Syllabus

Machine Learning

Syllabus 

Unit I:

The Machine Learning Landscape: What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning, Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating.

Unit II:

Classification: Training a Binary Classifier, Performance Measures, Measuring Accuracy Using Cross-Validation, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Error Analysis, Multilabel Classification, Multi Output Classification. k-NN Classifier.

Unit III:

Support Vector Machines: Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression, Under the Hood, Decision Function and Predictions, Training Objective, Quadratic Programming, The Dual Problem, Kernelized SVM, Online SVMs.

Unit IV:

Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexity, Gini Impurity or Entropy? Regularization Hyperparameters, Regression

Ensemble Learning and Random Forests: Voting Classifiers, Bagging and Pasting, Bagging and Pasting in Scikit-Learn, Out-of-Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extra-Trees, Feature Importance, Boosting, AdaBoost, Gradient Boosting, Stacking.

Unit V:

Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, Projection, PCA.

Clustering: How does clustering work: finding similarities using distances, Euclidean distance and other distance metrics. k-Means Clustering: Plotting customers with their segments, normalizing features, cluster centres and interpreting the Clusters. Hierarchical Clustering.

Textbooks:

1.     Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.

2.     Pradhan, Manaranjan, and U. Dinesh Kumar. Machine Learning using Python. Wiley, IIM Bangalore, 2019.

References:

 

1.     Introduction to Machine Learning, Ethem Alpaydin 2nd Edition, MIT Press 2000

2.     Machine Learning, Tom M. Mitchell, McGraw Hill, 1997, ISBN: 0-07-042807-7.

 

Machine Learning2: UNIT-5 Questions

UNIT-5 

Short Answer Questions

1. Write about control knowledge. 
2. Discuss Dynamic Programming 
3. Define explanation-based learning 
4. Define Reinforcement Learning 
5. Define Markov decision process. 
6. Define Agent, Environment, action. 

 Long Answer Questions

1. Explain Learning with Perfect Domain Theories: Prolog-EBG in detail. 

2. Discuss Explanation-Based learning of search control knowledge 

3. Explain about search control knowledge in detail 

4. Explain the inductive analytical approaches to learning 

5. Explain about PROLOG-EBG, in detail 

6. What are the differences between inductive learning and analytical learning problems and explain the same with an example. 

7. Illustrate Reinforcement Learning Problem with example. List the Reinforcement Learning Problem characteristics. 

8. Explain Q-Learning with an example 9. Illustrate Learning Task with an example. 

10. Explain temporal difference learning in detail. 

11. Discuss about Q–learning, in detail. 

12. Relationship to Dynamic Programming 

13. Nondeterministic Rewards and Actions,