|
1 | 1 | # `ML amendments`  |
2 | 2 | Contains coursework assignments in Masters made in latex. |
3 | 3 | Includes solved numericals, understanding questions and some extra topics. |
| 4 | + |
| 5 | +- `A1.1` - Machine Learning and its applications |
| 6 | +- `A1.2` - Least Mean Square (LMS) algorithm |
| 7 | +- `A1.3` - Confusion matrix and metrics |
| 8 | +- `A1.4` - Learning system for a _Tic-Tac-Toe_ player |
| 9 | +- `A2.1` - Match the followiing algorithms and loss functions to their classification counterparts |
| 10 | +- `A2.2` - The `Bias-Variance` tradeoff |
| 11 | +- `A2.3` - Categorical and Numerical features in a dataset |
| 12 | +- `A2.4` - Maximum Likelihood Estimates (MLE) for the _Univariate Gaussian Distribution_ |
| 13 | +- `A3.1` - Concept learning and related disciplines |
| 14 | +- `A3.2` - Use case of concept learning _Addison's disease_ |
| 15 | +- `A3.3` - `Find-S` algorithm and `Candidate-Elimination` algorithm |
| 16 | +- `A3.4` - Cross validation as an classifier evaluation technique |
| 17 | +- `A4.1` - Concept learning for `Decision Trees` |
| 18 | +- `A4.2` - Decision Tree basics for Machine Learning |
| 19 | +- `A4.3` - Feature selection and challenges for Decision trees ( _use case_ ) |
| 20 | +- `A4.4` - Iterative Dichotomiser-3 `ID-3` algorithm |
| 21 | +- `A5.1` - Overfitting in Decision Trees with relation to `Bias` & `Variance` |
| 22 | +- `A5.2` - Tree pruning for decision trees ( _Reduced Error Pruning_ ) |
| 23 | +- `A5.3` - Gain ratio as split measure |
| 24 | +- `A5.4` - Regression Trees |
| 25 | +- `A6.1` - Perceptron for classification |
| 26 | +- `A6.2` - The Perceptron training rule ( _Delta rule_ ) |
| 27 | +- `A6.3` - Neural Networks and its modalities |
| 28 | +- `A6.4` - Activation functions for Neural Networks ( _ReLU, Leaky ReLU variants_ ) |
| 29 | +- `A7.1` - Gradient descent training rule |
| 30 | +- `A7.2` - Proper `loss` functions for `activation` functions |
| 31 | +- `A7.3` - The Backpropogation algorithm  |
| 32 | +- `A7.4` - Effect of Learning rate as hyperparameter |
| 33 | +- `A8.1` - Non-sequential data classifiers, Feed-forward Neural Networks, BPTT, LSTM |
| 34 | +- `A8.2` - Naive bayes and Maximum-Aposteriori-Hypothesis (MAP) |
| 35 | +- `A8.3` - Naive Bayes ( _Numerical_ ) |
| 36 | +- `A8.4` - Spam classification `SpamAssassin` |
| 37 | +- `A9.1` - The `k-Nearest Neighbor Algorithm` |
| 38 | +- `A9.2` - Regression & Classification algorithms |
| 39 | +- `A9.3` - _k_-NN ( _Numerical_ ) |
| 40 | +- `A9.4` - Active Learning for Case-based reasoning |
| 41 | +- `A10.1` - Supervised vs. Unsupervised learning |
| 42 | +- `A10.2` - _k_ Means algorithm in action |
| 43 | +- `A10.3` - Hierachical Agglomerative Clustering algorithm |
| 44 | +- `A10.4` - Fuzzy-C-Means algorithm |
| 45 | +- `A11.1` - Learning Vector Quantization (LVQ) algorithm |
| 46 | +- `A11.2` - Reinforcement Learning and its components |
| 47 | +- `A11.3` - The `Value-Iteration` algorithm |
| 48 | +- `A11.4` - The `Value-Iteration` algorithm ( _Episodic process_ ) |
| 49 | +- `A12.1` - Association rules |
| 50 | +- `A12.2` - Frequent Itemset Mining ( _Exercise_ ) |
| 51 | +- `A12.3` - Support, Confidence measures for Arules ( _Numerical_ ) |
| 52 | +- `A12.4` - Apriori vs. ECLAT |
0 commit comments