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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
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.
