Machine Learning and Deep Learning培训

课程编码

mldt

课程时长

21 hours 通常来说是3天,包括中间休息。

要求

Basic knowledge of statistical concepts is desirable.

课程概览

本课程涵盖AI(强调Machine LearningDeep Learning

Machine Translated

课程大纲

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

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