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         Machine Learning and Deep Learning培訓

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

Logistic Regression

The Logistic Model cost function

Estimating the Coefficients

Making Predictions

Odds Ratio

Performance Evaluation Matrices

[Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]

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

Introduction to Deep Learning
ANN Structure

Biological neurons and artificial neurons

Non-linear Hypothesis

Model Representation

Examples & Intuitions

Transfer Function/ Activation Functions

Typical classes of network architectures

Feed forward ANN.

Structures of Multi-layer feed forward networks

Back propagation algorithm

Back propagation - training and convergence

Functional approximation with back propagation

Practical and design issues of back propagation 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

Resampling & Regularization

Note:

For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.

Analysis of different data sets will be performed using R