Artificial Neural Networks, Machine Learning and Deep Thinking Training Course
Course
In City Of London
Description
-
Type
Course
-
Location
City of london
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
Facilities
Location
Start date
Start date
Reviews
Subjects
- Probability
- Network Training
- Network
- Networks
Course programme
1. Understanding classification using nearest neighbors
- The kNN algorithm
- Calculating distance
- Choosing an appropriate k
- Preparing data for use with kNN
- Why is the kNN algorithm lazy?
- Basic concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability with Bayes' theorem
- The naive Bayes algorithm
- The naive Bayes classification
- The Laplace estimator
- Using numeric features with naive Bayes
- Divide and conquer
- The C5.0 decision tree algorithm
- Choosing the best split
- Pruning the decision tree
- Separate and conquer
- The One Rule algorithm
- The RIPPER algorithm
- Rules from decision trees
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
- Adding regression to trees
- From biological to artificial neurons
- Activation functions
- Network topology
- The number of layers
- The direction of information travel
- The number of nodes in each layer
- Training neural networks with backpropagation
- Classification with hyperplanes
- Finding the maximum margin
- The case of linearly separable data
- The case of non-linearly separable data
- Using kernels for non-linear spaces
- The Apriori algorithm for association rule learning
- Measuring rule interest – support and confidence
- Building a set of rules with the Apriori principle
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Using distance to assign and update clusters
- Choosing the appropriate number of clusters
- Working with classification prediction data
- A closer look at confusion matrices
- Using confusion matrices to measure performance
- Beyond accuracy – other measures of performance
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance tradeoffs
- ROC curves
- Estimating future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
- Using caret for automated parameter tuning
- Creating a simple tuned model
- Customizing the tuning process
- Improving model performance with meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
- Three Classes of Deep Learning
- Deep Autoencoders
- Pre-trained Deep Neural Networks
- Deep Stacking Networks
Artificial Neural Networks, Machine Learning and Deep Thinking Training Course