Machine Learning | Dr Jain Classes | L10 Unsupervised Learning paradigm | Handwritten notes

Welcome to Dr Jain Classes for CSE. This is the L10. Covered topics: 1. Nearest Neighbour Classifier 2. K Nearest Neighbour 3. When K is even and odd in KNN 4. Why K should be small? Implications of small and large K 5. Regression using KNN 6. Why KNN works well in close points or dense points? 7. we can say that we should have training samples well spread out in the input space or region. 8. For error to stay within a reasonable limit, your NNC will require an extraordinarily large number of training examples. 9. Curse of Dimensionality 10. VC Dimension for a regression problem 11. Decision Trees 12. Error in Decision Trees is wrt decision label 13. you assign the majority label to a set of examples so wrt to the majority label you will have an error in that node. 14. Error in Decision Trees 15. Worst case in Decision Trees 16. Idea of Decision Trees 17. Selecting an appropriate splitting criteria 18. ID3 algorithm 19. Greedy Algorithm and Decision Trees 20. Classifying test examples in decision trees 21. Will it be useful to use decision trees in real life? 22. Guard Against overfitting 23. Overfitting in Decision trees 24. How to avoid overfitting 25. Performance of SVM (Revision) 26. Regularization 27. Ensemble of Decision trees 28. Drawbacks of ID3 29. Random sampling 30. Random forest classifier 31. Neural Network / Deep Learning introduction 32. Perceptron Algorithm 33. Non-Linear Decision Boundary 34. Cross Entropy Loss 35. 0-1 loss 36. Softmax function 37. Feed Forward Neural Network 38. Training Algorithm 39. Backpropagation 40. Gradient Descent Derivation 41. Equation for Backward pass 42. GD vs SGD 43. Summary for MNIST 44. Data Normalization for Neural Network 45. Adjusting the learning rate (eta) 46. Wandering in space 47. Batch Size 48. Neurons Nonlinear computation units 49. Clustering 50. Similarity Measure 51. Distance function 52. Outcome of Clustering 53. Overlapping Clusters 54. Dendrograms 55. Hierarchical Clustering 56. Single Linkage CLustering 57. Max Linkage CLustering 58. Average Linkage Clustering 59. Neural Network as a universal approximation (IMP) 60. Boolean Algebra using Neural Network 61. Distortion measure for clustering 62. Formulation of Unsupervised clustering using graphs 63. Find partitions that minimize the cut value 64. the optimization problem in clustering 65. problem is to search for the optimal partitions of the graph into k sets of nodes. 66. Dimensionality reduction (no audio) Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/1e4SJ-PBPP2gDfTrRbB7EiGfX3vB2loXq/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff