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Course Outline
1. Grasping Classification via Nearest Neighbors
- The kNN algorithm
- Distance calculation methods
- Selecting an optimal k value
- Data preparation for kNN application
- Understanding the lazy nature of the kNN algorithm
2. Grasping Naive Bayes
- Core concepts of Bayesian methods
- Foundations of probability
- Joint probability
- Conditional probability through Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification techniques
- The Laplace estimator
- Applying numeric features with Naive Bayes
3. Grasping Decision Trees
- The divide and conquer approach
- The C5.0 decision tree algorithm
- Identifying the optimal split
- Pruning the decision tree
4. Grasping Classification Rules
- The separate and conquer strategy
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
5. Grasping Regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
6. Grasping Regression Trees and Model Trees
- Incorporating regression into trees
7. Grasping Neural Networks
- Transitioning from biological to artificial neurons
- Activation functions
- Network topology
- Determining the number of layers
- The direction of information flow
- The number of nodes per layer
- Training neural networks using backpropagation
8. Grasping Support Vector Machines
- Classification using hyperplanes
- Maximizing the margin
- Handling linearly separable data
- Handling non-linearly separable data
- Utilizing kernels for non-linear spaces
9. Grasping Association Rules
- The Apriori algorithm for association rule learning
- Evaluating rule interest through support and confidence
- Constructing a rule set using the Apriori principle
10. Grasping Clustering
- Clustering as a machine learning task
- The k-means clustering algorithm
- Using distance for cluster assignment and updates
- Determining the appropriate number of clusters
11. Measuring Performance for Classification
- Working with classification prediction data
- Examining confusion matrices in detail
- Utilizing confusion matrices for performance measurement
- Beyond accuracy – alternative performance measures
- 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
12. Tuning Stock Models for Enhanced Performance
- Using caret for automated parameter tuning
- Creating a simple tuned model
- Customizing the tuning process
- Enhancing model performance through meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
13. Deep Learning
- Three Classes of Deep Learning
- Deep Autoencoders
- Pre-trained Deep Neural Networks
- Deep Stacking Networks
14. Discussion of Specific Application Areas
21 Hours
Testimonials (1)
Very flexible.
