| Module Name | Download |
|---|---|
| noc19_cs52_assignment_Week_1 | noc19_cs52_assignment_Week_1 |
| noc19_cs52_assignment_Week_2 | noc19_cs52_assignment_Week_2 |
| noc19_cs52_assignment_Week_3 | noc19_cs52_assignment_Week_3 |
| noc19_cs52_assignment_Week_4 | noc19_cs52_assignment_Week_4 |
| noc19_cs52_assignment_Week_5 | noc19_cs52_assignment_Week_5 |
| noc19_cs52_assignment_Week_6 | noc19_cs52_assignment_Week_6 |
| noc19_cs52_assignment_Week_7 | noc19_cs52_assignment_Week_7 |
| noc19_cs52_assignment_Week_8 | noc19_cs52_assignment_Week_8 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 01: Introduction | Download |
| 2 | Lecture 02: Different Types of Learning | Download |
| 3 | Lecture 03: Hypothesis Space and Inductive Bias | Download |
| 4 | Lecture 04: Evaluation and Cross-Validation | Download |
| 5 | Tutorial I | Download |
| 6 | Lecture 05 : Linear Regression | Download |
| 7 | Lecture 06 : Introduction to Decision Trees | Download |
| 8 | Lecture 07 : Learning Decision Tree | Download |
| 9 | Lecture 08 : Overfitting | Download |
| 10 | Lecture 9: Python Exercise on Decision Tree and Linear Regression | Download |
| 11 | Tutorial II | Download |
| 12 | Lecture 12: k-Nearest Neighbour | Download |
| 13 | Lecture 13: Feature Selection | Download |
| 14 | Lecture 14: Feature Extraction | Download |
| 15 | Lecture 15: Collaborative Filtering | Download |
| 16 | Lecture 16: Python Exercise on kNN and PCA | Download |
| 17 | Lecture 17: Tutorial III | Download |
| 18 | Lecture 18: Bayesian Learning | Download |
| 19 | Lecture 19: Naive Bayes | Download |
| 20 | Lecture 20 : Bayesian Network | Download |
| 21 | Lecture 21: Python Exercise on Naive Bayes | Download |
| 22 | Lecture 22: Tutorial IV | Download |
| 23 | Lecture 23 : Logistic Regression | Download |
| 24 | Lecture 24: Introduction Support Vector Machine | Download |
| 25 | Lecture 25: SVM : The Dual Formulation | Download |
| 26 | Lecture 26: SVM : Maximum Margin with Noise | Download |
| 27 | Lecture 27: Nonlinear SVM and Kernel Function | Download |
| 28 | Lecture 28: SVM : Solution to the Dual Problem | Download |
| 29 | Lecture 29: Python Exercise on SVM | Download |
| 30 | Lecture 30: Introduction | Download |
| 31 | Lecture 31: Multilayer Neural Network | Download |
| 32 | Lecture 32 : Neural Network and Backpropagation Algorithm | Download |
| 33 | Lecture 33: Deep Neural Network | Download |
| 34 | Lecture 34: Python Exercise on Neural Network | Download |
| 35 | Lecture 35: Tutorial VI | Download |
| 36 | Lecture 36: Introduction to Computational Learning Theory | Download |
| 37 | Lecture 37: Sample Complexity : Finite Hypothesis Space | Download |
| 38 | Lecture 38: VC Dimension | Download |
| 39 | Lecture 39 : Introduction to Ensembles | Download |
| 40 | Lecture 40 : Bagging and Boosting | Download |
| 41 | Lecture 41 : Introduction to Clustering | Download |
| 42 | Lecture 42 : Kmeans Clustering | Download |
| 43 | Lecture 43: Agglomerative Hierarchical Clustering | Download |
| 44 | Lecture 44: Python Exercise on kmeans clustering | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 01: Introduction | Download To be verified |
| 2 | Lecture 02: Different Types of Learning | Download To be verified |
| 3 | Lecture 03: Hypothesis Space and Inductive Bias | Download To be verified |
| 4 | Lecture 04: Evaluation and Cross-Validation | Download To be verified |
| 5 | Tutorial I | Download To be verified |
| 6 | Lecture 05 : Linear Regression | Download To be verified |
| 7 | Lecture 06 : Introduction to Decision Trees | Download To be verified |
| 8 | Lecture 07 : Learning Decision Tree | Download To be verified |
| 9 | Lecture 08 : Overfitting | Download To be verified |
| 10 | Lecture 9: Python Exercise on Decision Tree and Linear Regression | Download To be verified |
| 11 | Tutorial II | Download To be verified |
| 12 | Lecture 12: k-Nearest Neighbour | Download To be verified |
| 13 | Lecture 13: Feature Selection | Download To be verified |
| 14 | Lecture 14: Feature Extraction | Download To be verified |
| 15 | Lecture 15: Collaborative Filtering | Download To be verified |
| 16 | Lecture 16: Python Exercise on kNN and PCA | Download To be verified |
| 17 | Lecture 17: Tutorial III | Download To be verified |
| 18 | Lecture 18: Bayesian Learning | Download To be verified |
| 19 | Lecture 19: Naive Bayes | Download To be verified |
| 20 | Lecture 20 : Bayesian Network | Download To be verified |
| 21 | Lecture 21: Python Exercise on Naive Bayes | Download To be verified |
| 22 | Lecture 22: Tutorial IV | Download To be verified |
| 23 | Lecture 23 : Logistic Regression | Download To be verified |
| 24 | Lecture 24: Introduction Support Vector Machine | Download To be verified |
| 25 | Lecture 25: SVM : The Dual Formulation | Download To be verified |
| 26 | Lecture 26: SVM : Maximum Margin with Noise | Download To be verified |
| 27 | Lecture 27: Nonlinear SVM and Kernel Function | Download To be verified |
| 28 | Lecture 28: SVM : Solution to the Dual Problem | Download To be verified |
| 29 | Lecture 29: Python Exercise on SVM | Download To be verified |
| 30 | Lecture 30: Introduction | Download To be verified |
| 31 | Lecture 31: Multilayer Neural Network | Download To be verified |
| 32 | Lecture 32 : Neural Network and Backpropagation Algorithm | Download To be verified |
| 33 | Lecture 33: Deep Neural Network | Download To be verified |
| 34 | Lecture 34: Python Exercise on Neural Network | Download To be verified |
| 35 | Lecture 35: Tutorial VI | Download To be verified |
| 36 | Lecture 36: Introduction to Computational Learning Theory | Download To be verified |
| 37 | Lecture 37: Sample Complexity : Finite Hypothesis Space | Download To be verified |
| 38 | Lecture 38: VC Dimension | Download To be verified |
| 39 | Lecture 39 : Introduction to Ensembles | Download To be verified |
| 40 | Lecture 40 : Bagging and Boosting | Download To be verified |
| 41 | Lecture 41 : Introduction to Clustering | Download To be verified |
| 42 | Lecture 42 : Kmeans Clustering | Download To be verified |
| 43 | Lecture 43: Agglomerative Hierarchical Clustering | Download To be verified |
| 44 | Lecture 44: Python Exercise on kmeans clustering | Download To be verified |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |