ML-latex-amendments
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Contains coursework assignments made in latex.
Machine Learning
Contains coursework assignments in Masters made in latex. Includes solved numericals, understanding questions and some extra topics.
-
A1.1
- Machine Learning and its applications -
A1.2
- Least Mean Square (LMS) algorithm -
A1.3
- Confusion matrix and metrics -
A1.4
- Learning system for a Tic-Tac-Toe player -
A2.1
- Match the followiing algorithms and loss functions to their classification counterparts -
A2.2
- TheBias-Variance
tradeoff -
A2.3
- Categorical and Numerical features in a dataset -
A2.4
- Maximum Likelihood Estimates (MLE) for the Univariate Gaussian Distribution -
A3.1
- Concept learning and related disciplines -
A3.2
- Use case of concept learning Addison's disease -
A3.3
-Find-S
algorithm andCandidate-Elimination
algorithm -
A3.4
- Cross validation as an classifier evaluation technique -
A4.1
- Concept learning forDecision Trees
-
A4.2
- Decision Tree basics for Machine Learning -
A4.3
- Feature selection and challenges for Decision trees ( use case ) -
A4.4
- Iterative Dichotomiser-3ID-3
algorithm -
A5.1
- Overfitting in Decision Trees with relation toBias
&Variance
-
A5.2
- Tree pruning for decision trees ( Reduced Error Pruning ) -
A5.3
- Gain ratio as split measure -
A5.4
- Regression Trees -
A6.1
- Perceptron for classification -
A6.2
- The Perceptron training rule ( Delta rule ) -
A6.3
- Neural Networks and its modalities -
A6.4
- Activation functions for Neural Networks ( ReLU, Leaky ReLU variants ) -
A7.1
- Gradient descent training rule -
A7.2
- Properloss
functions foractivation
functions -
A7.3
- The Backpropogation algorithm Video -
A7.4
- Effect of Learning rate as hyperparameter -
A8.1
- Non-sequential data classifiers, Feed-forward Neural Networks, BPTT, LSTM -
A8.2
- Naive bayes and Maximum-Aposteriori-Hypothesis (MAP) -
A8.3
- Naive Bayes ( Numerical ) -
A8.4
- Spam classificationSpamAssassin
-
A9.1
- Thek-Nearest Neighbor
algorithm -
A9.2
- Regression & Classification algorithms -
A9.3
- k-NN ( Numerical ) -
A9.4
- Active Learning for Case-based reasoning -
A10.1
- Supervised vs. Unsupervised learning -
A10.2
- k Means algorithm in action -
A10.3
- Hierachical Agglomerative Clustering algorithm -
A10.4
- Fuzzy-C-Means algorithm -
A11.1
-Learning Vector Quantization (LVQ)
algorithm -
A11.2
- Reinforcement Learning and its components -
A11.3
- TheValue-Iteration
algorithm -
A11.4
- TheValue-Iteration
algorithm ( Episodic process ) -
A12.1
- Association rules -
A12.2
- Frequent Itemset Mining ( Exercise ) -
A12.3
- Support, Confidence measures for Arules ( Numerical ) -
A12.4
- Apriori vs. ECLAT