Sally Kang

why join the navy.

Notes for the introduction of AI

29 Jun 2018

[ AI  Algorithms  ]

AI Introduction

What is AI

Game playing

Why in AI

What Characteristics

What types studied in this lecture:


How to eaxm?

  1. computes the backed-up values
  2. show intermediate bounding values and the branches that would be pruned.
  3. show the move
  4. Expectiminimax ? Operators,Utility function,Evaluation functionGame tree (What representations)

Machine learning

what is it

what types


How to exam

  1. predict examples using knn

  2. predict examples using NB

  3. predict examples using 1R

  4. laplace correction

  5. NB deal with numeric attributes - probability density function, normal distribution

  6. knn deal with numeric attributes

  7. How to buld DT
    • How to Find the Best Attribute
  8. explain and calculate Entropy

  9. explain and calculate information gain

  10. explain and calculate Gain Ratio

  11. what is support vector

  12. how to calculate margin

  13. what is Maximum Margin Hyperplane

  14. why Maximum Margin?

  15. using the Lagrange multipliers to calculate decision boundry

  16. explain Soft Margin

  17. Kernel Trick

  18. explain Ensemble of Classifiers

  19. When Ensembles Work Well?

  20. When is Bagging Useful?

  21. Similarities & Differences for bagging and boosting

The area of AI to write programs that can learn from

• examples • domain knowledge • user feedback (what is it)

Neural networks

What is an ANN

What types



How to exam

  1. predicting examples using Single-Neuron Perceptron
  2. Unified Learning Rule Formula
  3. Multi-layer Perceptron Network – Architecture
    • Number of Input Neurons
    • Numerical
    • Nominal
    • Number of Hidden Layers and Neurons
  4. Error Function formula
  5. explain Steepest Gradient Descent
  6. all Backpropagation process formulas
  7. what is steepest Gradient Descent used for?
  8. explain Steepest Gradient Descent and momentum
  9. explain and compute Autoencoder NN
  10. training steps of deep NN
  11. explain Stacked Autoencoders
  12. brief explain of Convolutional networks

Probabilistic reasoning and inference

Probability basics

Bayesian networks

how to exam

  1. Probability basics formula

  2. calculate Bayesian Networks

  3. calculate Probabilistic reasoning

  4. draw CPT

  5. Using Bayesian networks for classification

Unsupervised learning

What is Clustering

what types


How to exam

  1. explain and calculate Centroid (means) , Medoid M, Single link, Complete link, Average link
  2. What is a Good Clustering?
  3. Davies-Bouldin (DB) index formula
  4. calculate k-means
  5. calculate Nearest Neighbor Clustering

Usual issuses

Need for Normalization

High Dimensionality

Dealing with Missing Values

Dealing With Numeric Attributes


Handling Attributes with Different Costs

Evaluating and Comparing


what is Holdout Procedure

what is Repeated Holdout Method

whats difference between Repeated Holdout Method and cross validation,

what is Cross-Validation

what is Validation Set, what for, which uses

what is Stratification


what is Confusion Matrix

formula of recall (R), precision (P) F1 measure (F1)

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