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ranjiGT/ML-latex-amendments: Contains coursework assignments made in latex.

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

ranjiGT/ML-latex-amendments

开源软件地址(OpenSource Url):

https://github.com/ranjiGT/ML-latex-amendments

开源编程语言(OpenSource Language):

TeX 100.0%

开源软件介绍(OpenSource Introduction):

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 - The Bias-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 and Candidate-Elimination algorithm
  • A3.4 - Cross validation as an classifier evaluation technique
  • A4.1 - Concept learning for Decision Trees
  • A4.2 - Decision Tree basics for Machine Learning
  • A4.3 - Feature selection and challenges for Decision trees ( use case )
  • A4.4 - Iterative Dichotomiser-3 ID-3 algorithm
  • A5.1 - Overfitting in Decision Trees with relation to Bias & 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 - Proper loss functions for activation 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 classification SpamAssassin
  • A9.1 - The k-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 - The Value-Iteration algorithm
  • A11.4 - The Value-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



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