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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 the Regression Models
- Qualitative Predictors
- Extensions of the 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
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Logistic Regression
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The Logistic Model cost function
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Estimating the Coefficients
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Making Predictions
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Odds Ratio
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Performance Evaluation Matrices
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[Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]
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Multiple Logistic Regression
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Logistic Regression for >2 Response Classes
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Regularized Logistic Regression
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Linear Discriminant Analysis
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Using Bayes’ Theorem for Classification
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Linear Discriminant Analysis for p=1
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Linear Discriminant Analysis for p >1
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Quadratic Discriminant Analysis
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K-Nearest Neighbors
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Classification with Non-linear Decision Boundaries
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Support Vector Machines
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Optimization Objective
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The Maximal Margin Classifier
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Kernels
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One-Versus-One Classification
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One-Versus-All Classification
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Comparison of Classification Methods
Introduction to Deep Learning
ANN Structure
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Biological neurons and artificial neurons
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Non-linear Hypothesis
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Model Representation
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Examples & Intuitions
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Transfer Function/ Activation Functions
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Typical classes of network architectures
Feed forward ANN.
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Structures of Multi-layer feed forward networks
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Back propagation algorithm
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Back propagation - training and convergence
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Functional approximation with back propagation
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Practical and design issues of back propagation learning
Deep Learning
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Artificial Intelligence & Deep Learning
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Softmax Regression
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Self-Taught Learning
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Deep Networks
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Demos and Applications
Lab:
Getting Started with R
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Introduction to R
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Basic Commands & Libraries
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Data Manipulation
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Importing & Exporting data
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Graphical and Numerical Summaries
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Writing functions
Regression
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Simple & Multiple Linear Regression
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Interaction Terms
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Non-linear Transformations
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Dummy variable regression
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Cross-Validation and the Bootstrap
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Subset selection methods
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Penalization [Ridge, Lasso, Elastic Net]
Classification
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Logistic Regression, LDA, QDA, and KNN,
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Resampling & Regularization
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Support Vector Machine
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Resampling & Regularization
Note:
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For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.
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Analysis of different data sets will be performed using R
Requirements
Basic knowledge of statistical concepts is desirable.
21 Hours
Testimonials (4)
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.
