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Course Outline
Machine Learning
Introduction to Machine Learning
- Applications of machine learning
- Supervised versus unsupervised learning
- Machine learning algorithms
- Regression
- Classification
- Clustering
- Recommender System
- Anomaly Detection
- Reinforcement Learning
Regression
- Simple & Multiple Regression
- Least Square Method
- Estimating the Coefficients
- Assessing the Accuracy of the Coefficient Estimates
- Assessing the Accuracy of the Model
- Post Estimation Analysis
- Other Considerations in Regression Models
- Qualitative Predictors
- Extensions of Linear Models
- Potential Problems
- Bias-variance trade-off (under-fitting/over-fitting) for regression models
Resampling Methods
- Cross-Validation
- The Validation Set Approach
- Leave-One-Out Cross-Validation
- k-Fold Cross-Validation
- Bias-Variance Trade-Off for k-Fold
- The Bootstrap
Model Selection and Regularization
- Subset Selection
- Best Subset Selection
- Stepwise Selection
- Choosing the Optimal Model
- Shrinkage Methods/Regularization
- Ridge Regression
- Lasso & Elastic Net
- Selecting the Tuning Parameter
- Dimension Reduction Methods
- Principal Components Regression
- Partial Least Squares
Classification
Logistic Regression
- The Logistic Model Cost Function
- Estimating the Coefficients
- Making Predictions
- Odds Ratio
- Performance Evaluation Matrices
- Sensitivity/Specificity/PPV/NPV
- Precision
- ROC Curve
- Multiple Logistic Regression
- Logistic Regression for >2 Response Classes
- Regularized Logistic Regression
Linear Discriminant Analysis
- Using Bayes’ Theorem for Classification
- Linear Discriminant Analysis for p=1
- Linear Discriminant Analysis for p>1
Quadratic Discriminant Analysis
K-Nearest Neighbors
- Classification with Non-Linear Decision Boundaries
Support Vector Machines
- Optimization Objective
- The Maximal Margin Classifier
- Kernels
- One-Versus-One Classification
- One-Versus-All Classification
Comparison of Classification Methods
Deep Learning
Introduction to Deep Learning
Artificial Neural Networks (ANNs)
- Biological neurons and artificial neurons
- Non-linear Hypothesis
- Model Representation
- Examples & Intuitions
- Transfer Function/Activation Functions
- Typical Classes of Network Architectures
- Feedforward ANN
- Multi-layer Feedforward Networks
- Backpropagation Algorithm
- Backpropagation - Training and Convergence
- Functional Approximation with Backpropagation
- Practical and Design Issues of Backpropagation Learning
Deep Learning
- Artificial Intelligence & Deep Learning
- Softmax Regression
- Self-Taught Learning
- Deep Networks
- Demos and Applications
Lab:
Getting Started with R
- Introduction to R
- Basic Commands & Libraries
- Data Manipulation
- Importing & Exporting Data
- Graphical and Numerical Summaries
- Writing Functions
Regression
- Simple & Multiple Linear Regression
- Interaction Terms
- Non-Linear Transformations
- Dummy Variable Regression
- Cross-Validation and the Bootstrap
- Subset Selection Methods
- Penalization (Ridge, Lasso, Elastic Net)
Classification
- Logistic Regression, LDA, QDA, and KNN
- Resampling & Regularization
- Support Vector Machine
Notes:
- For ML algorithms, case studies will be used to discuss their application, advantages, and potential issues.
- Analysis of different datasets will be performed using R.
Requirements
- Basic knowledge of statistical concepts is desirable
Audience
- Data scientists
- Machine learning engineers
- Software developers interested in AI
- Researchers working with data modeling
- Professionals looking to apply machine learning in business or industry
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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