Mixed Initiative Planning and Dialog managment

Mixed-initiative planning (MIP) is one approach to integrate or involve user through dialog management in the planning process, that is, in solving a user problem. Planning is the process of finding a sequence of actions that will achieve the goal given the initial state. PDDL (Planning Domain Definition Language) or its variant is used to describe the problem. Once the problem is fully describe in PDDL, we now know the four things about the problem: the initial state, the action that can be taken in any state, the result of taking such action and the goal state. Planning graph is a special data structure that represent those four things.

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Decision Tree

Decision tree is a multi-class classification tool allowing a data point to be classified into one of many (two or more) classes available. A decision tree divides the sample space into a rectilinear region. This will be more clear with an example. Let us say we have this auto-insurance claim related data as shown in the following table. We want to predict what type of customer profile may more likely lead to claim payout. The decision tree model may first divide the sample space based on age. So, now we have two regions divided based on the age. Next, one of those regions will further sub-divided based Marital_status, and then that newly divided sub-regision may further get divide based on Num_of_vehicle_owned. A decision tree is made up of a root node followed by intermediate node and leaf node. Each leaf node represents one of the class into which data points have been classified to. An intermediate node represents the decision rule based...

Recommender System using Collaborative filtering

Recommender system using collaborative filtering approach uses the past users' behavior to predict what items the current user would like. We create a UxM matrix where U is the number of users and M is the number of different items or products. Uij is the rating expressed by the user-i for product-j. In the real world, not every user expresses an opinion about every product. For example, let us say there are five users including Bob has expressed their opinion about four movies as shown below Table 1: movie1 movie2 movie3 movie4 user1 1 3 3 5 user2 2 4 5 user3 3 2 2 user4 1 3 4 Bob 3 2 5 ? Our goal is to predict what movies to recommend to Bob, or put it another way should we recommend movie4 to Bob, knowing the rating for four movies from other users including Bob. Traditionally, we could do item to item comparison, which means if the user has liked item1 in the past then that user may like other items similar to item1. Another way to recommend...

Sentimental Analysis Using Scikit-Learn and Neural Network

Using Scikit-Learn and NLTK for Sentimental Analysis Sentimental analysis is a way of categorizing text into subgroup based on the opinion or sentiments expressed in the text. For example, we would like to categorize review or comments of people about a movie to determine how many like the movie and how many don't. In a supervised sentimental analysis, we have some training data which is already categorized or sub-grouped into different categories, for example, into 'positive' or 'negative' sentiments. We used these training data to train our model to learn what makes a text to be part of a specific group. By text I mean a sentence or a paragraph. Using this labeled sentences, we are going to build a model. So, let us say we have following training text: training_positive = list() training_positive[0] = "bromwell high is a nice cartoon comedy perfect for family" training_positive[1] = " homelessness or houselessness as george carlin s...

Machine Learning

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