Course Outline
Machine Learning
Introduction to Machine Learning
- Applications of machine learning
- Supervised versus unsupervised learning
- Machine learning algorithms
- Regression
- Classification
- Clustering
- Recommender Systems
- Anomaly Detection
- Reinforcement Learning
Regression
- Simple and Multiple Regression
- Least Squares Method
- Estimating Coefficients
- Assessing the Accuracy of Coefficient Estimates
- Assessing Model Accuracy
- Post-Estimation Analysis
- Other Considerations in Regression Models
- Qualitative Predictors
- Extensions of Linear Models
- Potential Issues
- 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 Regularisation
- Subset Selection
- Best Subset Selection
- Stepwise Selection
- Choosing the Optimal Model
- Shrinkage Methods/Regularisation
- Ridge Regression
- Lasso and Elastic Net
- Selecting the Tuning Parameter
- Dimension Reduction Methods
- Principal Components Regression
- Partial Least Squares
Classification
Logistic Regression
- The Logistic Model Cost Function
- Estimating Coefficients
- Making Predictions
- Odds Ratio
- Performance Evaluation Matrices
- Sensitivity/Specificity/PPV/NPV
- Precision
- ROC Curve
- Multiple Logistic Regression
- Logistic Regression for >2 Response Classes
- Regularised 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 Neighbours
- Classification with Non-Linear Decision Boundaries
Support Vector Machines
- Optimisation 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 and 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 and Deep Learning
- Softmax Regression
- Self-Taught Learning
- Deep Networks
- Demos and Applications
Lab:
Getting Started with R
- Introduction to R
- Basic Commands and Libraries
- Data Manipulation
- Importing and Exporting Data
- Graphical and Numerical Summaries
- Writing Functions
Regression
- Simple and Multiple Linear Regression
- Interaction Terms
- Non-Linear Transformations
- Dummy Variable Regression
- Cross-Validation and the Bootstrap
- Subset Selection Methods
- Penalisation (Ridge, Lasso, Elastic Net)
Classification
- Logistic Regression, LDA, QDA, and KNN
- Resampling and Regularisation
- Support Vector Machine
Notes:
- For machine learning 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 with an interest in AI
- Researchers working on data modelling
- Professionals seeking to apply machine learning in business or industry contexts
Testimonials (6)
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning
Last day with the AI
Ovidiu - DB Global Technology SRL
Course - Machine Learning and Deep Learning
The examples that were picked, shared with us and explained
Cristina - DB Global Technology SRL
Course - Machine Learning and Deep Learning
I really enjoyed the coverage and depth of topics.
Anirban Basu
Course - Machine Learning and Deep Learning
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course - Machine Learning and Deep Learning
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.
