Cogsci 1 UC Berkeley Summer 2024/2025 Final | Questions and Correct Solutions | Latest Update | Graded A+

Cogsci 1 UC Berkeley Summer 2024/2025 Final | Questions and Correct Solutions | Latest Update | Graded A+ Expert Systems - Answer -Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. (like MYCIN at Stanford in the early 1970's). It was more accurate than human experts. decision trees - Answer -Diagrams where answers to yes or no questions lead decision makers to address additional questions until they reach the end of the tree. Basic IF-THEN rules. terminal leaf or node - Answer -the leaf at the end of a branch - the decision / outcome in a decision tree Classification algorithm - Answer -A decision tree is like a classification algorithm in that it divides down the population - it classifies (classifying system). Machine Learning - Answer -the extraction of knowledge from data based on algorithms created from training data (sub-field of expert systems)ID3 algorithm (for machine learning) - Answer -The ID3 algorithm works by assigning attributes to nodes. It identifies, for each node in the decision tree, which attribute would be most informative at that point, called information gain. Information gain measures how well a particular attribute classifies a set of examples. At each node, the algorithm chooses the remaining attribute with the highest information gain. target attribute - Answer -attribute you are most interested in when classifying examples (objects in your database) - for example if Loan is the target attribute the two possible values