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Introduction to Machine Learning

This class was created by Brainscape user Neil Umoh. Visit their profile to learn more about the creator.

By: Neil Umoh
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Decks in this class (19)

2-mlconcepts
What is an instance,
What is an instance also known as,
What is an attribute
22  cards
3-probability
What is bayes rule,
What is posterior probability,
What is prior probability
11  cards
4-Decision Trees
How easy is it to construct decis...,
What is information gain,
What is entropy
15  cards
5-KNN
What is the knn algorithm,
How are data points represented i...,
What is hamming distance
17  cards
6-ExactOptimisation
How do we find optimal point,
Why can we take the log of the li...,
How do we optimise with constraints
4  cards
7-Naive Bayes
What is the naive assumption in n...,
What is the naive bayes algorithm,
What are the naive bayes assumptions
5  cards
8-Model evaluation 1
What is holdout evaluation strategy,
What are the advantages of holdou...,
What are the disadvantages of hol...
27  cards
9-featureselection
What are the two main ways to do ...,
What are wrapper methods,
What are the advantages of wrappe...
22  cards
10-Iterative optimisation
What is iterative optimisation,
What is gradient descent,
What is the issue with gradient d...
5  cards
11-logistic regression
What is logistic regression,
What are the log odds,
What is the logistic regression f...
11  cards
12-perceptron
What is the perceptron,
What is a neuron,
What is the perceptron model
10  cards
13-neural networks
What is a neural network,
What are the three types of layer...,
What is linear classification
12  cards
14-backpropagation
Why can t we use the perceptron r...,
What is a backpropagation
2  cards
15-EvaluationII
What is the generalised error for...,
What is model bias,
What is model variance
11  cards
17-Unsupervised learning
What is unsupervised learning,
What is the difference in exclusi...,
What is the difference between de...
21  cards
18-Semi supervised learning
What is the basis behind active l...,
What is query synthesis,
What is stream based synthesis
16  cards
19-Ensembe learning
What is ensemble learning,
What are the approaches for ensem...,
What is instance manipulation
25  cards
20-anomaly detection
What is an outlier anomoly,
What are applications of anomaly ...,
What are the types of anomaly
22  cards
21-Mitigating algorithmic bias
What is out group homogeneity bias,
What is correlation fallacy,
What is historical bias
11  cards

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Introduction to Machine Learning

  • Class purpose General learning

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