Page 14 - Artificial Intellegence_v2.0_Class_11
P. 14

Correlation and Regression                    Knowledge – Correlations, Regression, and other related terms
                          o   Crosstabs and scatterplots                Applications – Being able to relate data with regression and
                          o   Pearson's r                               correlation. Everyday applications of these mathematical
                          o   Regression - Finding the line             concepts.
                          o   Regression - Describing the line
                            o   Regression - How good is the line?
                   REGRESSION       o   Example contingency table
                             Correlation is not causation
                          o
                          o
                             Example Pearson's r and regression Readings
                          o o  Correlation
                             Regression
                          o   Caveats and examples
                          o   Practice exercise Correlation and Regression
                          o   Explain the importance of data from above examples
                          o   How prediction changes with changing data?
                          What is a classification problem?             Knowledge – What is classification and its types, what kind of
                          Examples                                      problems may be placed under the category of a classification
                          - Simple binary classification
                           Introduction to binary classification with logistic regression  problem
                   CLASSIFICATION&CLUSTERING        Practice exercise on simple Binary Classification model  society
                                                                        Applications – Where to apply classification principals
                                                                        Analysis – Impact of the application of incorrect algorithms on
                           True positives, true negatives, false positives and false
                          negatives
                                                                        Knowledge – Clustering problems and its application, why is it
                             Where we should care more with examples
                          o
                                                                        called clustering
                          o
                               Example- false negative of a disease detection can have
                             different implication than false positive, one will be
                                                                        Application – Application of clustering problem using standard
                             more physical harm and other will be mental
                                                                        models
                          What is a clustering problem?
                          Why is it unsupervised?
                          Examples
                          Practice exercise on simple Clustering model  Knowledge – What is ethics, Impact of ethics on society, the
                          AI working for good
                  AI VALUES (BIAS   AWARENESS)    Principles for ethical AI  impact of bias on AI functioning
                                                                        Evaluation – Biases in data, how to de-bias or neutralize the
                          Types of bias (personal /cultural /societal)
                                                                        biased data
                          How bias influences AI based decisions
                                                                        Application – Finding bias in acquired dataset
                          How data driven decisions can be debiased
                          Hands on exercise to Detect the Bias (Intro to AI)































                                                              (xii)
   9   10   11   12   13   14   15   16   17   18   19