Megha Sharma

artificial intelligence data science ai business intelligence omega teched c programming tyit sem6 model evaluation matrix operators in c dbms steps in knowledge engineering knowledge engineering knowledge management process decision trees supervised and unsupervised learning machine learning data discrete vs continuous local beam search first order logic environment data pipelines loading transformation extraction etl data management activities standard deviation model spice model median mean auc roc curve confusion matrix f1 score recall precision accuracy line plot bar plot scatter plot box plot exploratory data analysis eda histogram visualization techniques data transformation data preprocessing data cleaning data procession merging feature selection data preprocession. quantitative data qualitative data different types of data tycs knowledge information big data boosting bagging ensemble learning association rule in ml confidence lift support association rule mining bellman's equation features reinforcement learning expectation maximization e m algorithm information gain information technology entropy unsupervised learning decision tree types of machine learning nested if if else ladder if slse if statements in c special operators conditional operators assignment operators bitwise operators logical operators relational operators increment decrement operators arithmetic operators structure of c program keywords identifiers algorithm & flowchart c programming omega teched aggregation generalization specialization extended er model enhanced er model eer binary vs ternary relationship entity vs relationship entity vs attributes er design issues total participation partial participation participation constraints in er diagram many to many many to one one to many one to one mapping cardinalities n-ary relationship ternary binary unary degree of relationship set relationship types of attributes er models entity relationship durability consistency isolation atomicity transaction management acid properties limitation of dbms advantages of dbms dbms schema database advantages types of database database tybsc it sem5 unification generalized modus ponens existential instantition universal instantiation inference rules for quantifiers circuit domain tyit sem5 modus pronens inference engine inference engine approaches backward chaining forward chaining raster and vector display. applications of computer graphics applications. introduction to computer graphics limitations advantages development of expert system expert system artificial vs natural intelligence. machine learning vs deep learning process and practice approach. approaches knowledge management activities tacit and explicit knowledge information and knowledge relationship between data knowledge management optimization models for logistic planning supply chain management logistic and production models sales force management. up-selling cross selling tasks of relational marketing relational marketing marketing intelligence marketing models divisive approach agglomerative hierarchical clustering types of clustering k-means clustering clustering support vector machine. artificial neural network logistic regression classification algorithm naive baye's classifier. k-nearest neighbors tyit sem-6 regression classification six step crisp-dm data mining applications how data mining works data mining optimization model risk analysis pattern recognition predictive business intelligence. classes of model dynamic static stochastic deterministic symbolical analogical iconic types of mathematical model limitations of dss phases in the development of dss objectives of dss components of dss decision support system etl process components of bi architecture of bi mathematical model information knowledge introduction to bi uninformed search in ai dfs bfs bidirectional search depth first search breadth first search uninformed search known vs unknown single agent vs multiagent episodic vs sequential deterministic vs stochastic static vs dynamic fully observable vs partially observable types of environment in ai wumpus worls structure of agents model based agents reflex agents 8 queens problem 8 puzzle problems problem solving agents simulated annaling hill climbing local search propositional logic knowledge based agents introduction what is ai introduction to ai artificial search informed search best first search history of ai learning agent utility agent goal based agent genetic algoritjm foundations of artificial intelligence properties of quantifier predicate logic quantifiers elements of fol a start search with examples informed search in ai a start search artificial intelligence applications application of ai example of and-or search and or search alpha beta pruning peas rational agents agent program agent functions sensors agents optimal decision in games problem formulation game tree adversarial search dcl tcl dml ddl sybcom syit database languages relational table join relational database rdbms