The personal coder
Everybody could do with their own assistant. Speech recognition based software such as Alexa, Siri and Cortana are able to understand simple commands. While they know how to locate an item of interest they can only serve what is readily available either in context of the device or online from the world wide web. Instructions like a one-word genre of music is automatically translated to playing that music from the genre in the collection available to it.
These assistants are being made smarter to understand the relevance of the instruction and to narrow down the execution of the command to improve satisfaction. Some of these techniques have utilized artificial intelligence and the abilities to group, sort, rank, learn with neurons and make recommendations. There is definitely a lot of improvements on the horizon here given that we have just recently started making this territory mainstream.
However, I introduce the notion of a personal automation assistant, when we are not just looking for one item and we have more than one task to sequence, then giving repeated instructions itself becomes a chore. Try saying play violin ten times and you need an integrator along with a command to play it once. Similarly, if you need the result of one task to be taken as the input for the second, then we require a script that knows how to integrate between two actions. Expand the definition of instructions to be able to do assorted tasks such as turn on the toaster or strand music to the bathroom via heterogeneous systems and you need a software solution integrator to write code to make that automation.
Fortunately, nowadays devices and applications can work independently while allowing scriptable programmatic access for remote invocation. Even the language of interaction has become standard and easy to be invoked over what are called REST based APIs. These APIs follow well defined conventions and executing a task merely translates to making one or more API calls in the correct form and order. These APIs can be further explored for their corresponding resource with the help of a technique that makes them readable like a book. Therefore, the availability of standard API and their invocations is straightforward task of mix and match which can be folded into the portfolio of tasks that these assistants perform. Hence the notion of a personal coder.
It may behoove us to note that coding is one of the natural and immensely expansive capability that can be added to the assistant. There are many more roles other than a coder that an be folded into repertoire of tasks that the assistant may assume. Professionals such as accounting, transcribing, remote management are merely describable as automatable tasks and consequently roles for the assistant. Finally, ‘the’ becomes equivalent to an irreplaceable assistant.
The emphasis made here is about making API calls over the HTTP by picking the right API and supplying the right parameters as an example for the coding assistant. Scripts that are as easily written as a single curl command are probably more suited for this kind of assistant. For complex operations, we naturally want manual intervention. Another way to look at improving the assistant's capabilities is to make more data sources available for the already smooth-running operations of the assistant. For example, Google used to ship search appliances that worked on the customer's premise for their proprietary data. We could now use a similar concept for the voice activated assistant.

We were discussing modeling. A model articulates how a system behaves quantitatively. Models use numerical methods to examine complex situations and come up with predictions. Most common techniques involved for coming up with a model include statistical techniques, numerical methods, matrix factorizations and optimizations.  
Sometimes we relied on experimental data to corroborate the model and tune it. Other times, we simulated the model to see the predicted outcomes and if it matched up with the observed data. There are some caveats with this form of analysis. It is merely a representation of our understanding based on our assumptions. It is not the truth. The experimental data is closer to the truth than the model. Even the experimental data may be tainted by how we question the nature and not nature itself.  This is what Heisenberg and Covell warn against. A model that is inaccurate may not be reliable in prediction. Even if the model is closer to truth, garbage in may result in garbage out
Any model has a test measure to determine its effectiveness. since the observed and the predicted are both known, a suitable test metric may be chosen. for example the sum of squares of errors or the F-measure may be used to compare and improve systems.
#codingexercise 
implement the fix centroid step of k-means
bool fix_centroids(int dimension, double** vectors, int* centroids, int* cluster_labels, int size, int k)

{
    bool centroids_updated = false;
    int* new_centroids = (int*) malloc(k * sizeof(int));
    if (new_centroids == NULL) { printf("Require more memory"); exit(1);}
    for (int i = 0; i < k; i++)
    {
        int label = i;
        double minimum = 0;
        double* centroid = vectors[centroids[label]];
        for (int j = 0; j < size; j++)
        {
             if (j != centroids[label] && cluster_labels[j] == label)
             {
                double cosd = get_cosine_distance(dimension, centroid, vectors[j]);
                minimum += cosd * cosd;
             }
        }

        for (int j = 0; j < size; j++)
        {
             if (cluster_labels[j] != label) continue;
             double distance = 0;
             for (int m = 0; m < size; m++)
             {
                if (cluster_labels[m] != label) continue;
                if (m == j) continue;
                double cosd = get_cosine_distance(dimension, vectors[m], vectors[j]);
                distance += cosd * cosd;
             }

             if (distance < minimum)
             {
                 minimum = distance;
                 new_centroids[label] = j;
                 centroids_updated = true;
             }
        }
    }

    if (centroids_updated)
    {
        for (int j = 0; j < k; j++)
        {
            centroids[j] = new_centroids[j];
        }
    }
    free(new_centroids);
    return centroids_updated;

}

we were discussing modeling in general terms. We will be following slides from Costa, Kleinstein and Hershberg on Model fitting and error estimation.
A model articulates how a system behaves quantitatively. Models use numerical methods to examine complex situations and come up with predictions. Most common techniques involved for coming up with a model include statistical techniques, numerical methods, matrix factorizations and optimizations.  
Sometimes we relied on experimental data to corroborate the model and tune it. Other times, we simulated the model to see the predicted outcomes and if it matched up with the observed data. There are some caveats with this form of analysis. It is merely a representation of our understanding based on our assumptions. It is not the truth. The experimental data is closer to the truth than the model. Even the experimental data may be tainted by how we question the nature and not nature itself.  This is what Heisenberg and Covell warn against. A model that is inaccurate may not be reliable in prediction. Even if the model is closer to truth, garbage in may result in garbage out
#codingexercise
assign clusters to vectors as part of k-means:
void assign_clusters(int dimension, double** vectors, int* centroids, int* cluster_labels, int size, int k)

{

    for (int m = 0; m < size; m++)

    {

        double minimum = get_cosine_distance(dimension, vectors[centroids[cluster_labels[m]]], vectors[m]);

        for (int i = 0; i < k; i++)

        {

             double* centroid = vectors[centroids[i]];

             double distance = get_cosine_distance(dimension, centroid, vectors[m]);

             if (distance < minimum)

             {

                 cluster_labels[m] = i;

             }

        }

    }

}

We were discussing the difference between data mining and machine learning given that there is some overlap. In this context, I want to attempt explaining modeling in general terms. We will be following slides from Costa, Kleinstein and Hershberg on Model fitting and error estimation.
A model articulates how a system behaves quantitatively. It might involve equations or a system of equations using variables to denote the observed. The purpose of the model is to give a prediction based on the variables. In order to make the prediction somewhat accurate, it is often trained on a set of data before being used to predict on the test data. This is referred to as model tuning. Models use numerical methods to examine complex situations and come up with predictions. Most common techniques involved for coming up with a model include statistical techniques, numerical methods, matrix factorizations and optimizations.  Starting from the Newton's laws, we have used this kind of technique to understand and use our world.
Sometimes we relied on experimental data to corroborate the model and tune it. Other times, we simulated the model to see the predicted outcomes and if it matched up with the observed data. There are some caveats with this form of analysis. It is merely a representation of our understanding based on our assumptions. It is not the truth. The experimental data is closer to the truth than the model. Even the experimental data may be tainted by how we question the nature and not nature itself.  This is what Heisenberg and Covell warn against. A model that is inaccurate may not be reliable in prediction. Even if the model is closer to truth, garbage in may result in garbage out.

#codingexercise

Get Tanimoto coefficient between two vectors

double get_tanimoto_coefficient(int dimension, double* vec1, double* vec2)
{

double numerator = 0;

double denominator = 0;
double dotProduct = 0;
double magnitude = 0;
int i, j;
    for (i = 0; i < dimension; i++) dotProduct += vec1[i] * vec2[i];
numerator = dotProduct;
    for (i = 0; i < dimension; i++) magnitude += vec1[i] * vec1[i];

denominator += magnitude;
    magnitude = 0;
    for (i = 0; i < dimension; i++) magnitude += vec2[i] * vec2[i];

denominator += magnitude;

denominator -= dotProduct;

if (denominator == 0) return 0;

return numerator/denominator;

}

Tanimoto coefficient looks awfully similar to cosine distance but they have very different meanings and have little similarity other than expression syntax 

We were discussing the difference between data mining and machine learning given that there is some overlap. In this context, I want to attempt explaining modeling in general terms. We will be following slides from Costa, Kleinstein and Hershberg on Model fitting and error estimation.
A model articulates how a system behaves quantitatively. It might involve equations or a system of equations using variables to denote the observed. The purpose of the model is to give a prediction based on the variables. In order to make the prediction somewhat accurate, it is often trained on a set of data before being used to predict on the test data. This is referred to as model tuning. Models use numerical methods to examine complex situations and come up with predictions. Most common techniques involved for coming up with a model include statistical techniques, numerical methods, matrix factorizations and optimizations.  Starting from the Newton's laws, we have used this kind of technique to understand and use our world.


#codingexercise
Describe the k-means clustering technique
void kmeans(int dimension, double **vectors, int size, int k, int max_iterations)
{
     if (vectors == NULL || *vectors == NULL || |size == 0 || k == 0 || dimension  == 0) return;

     int* cluster_labels = initialize_cluster_labels(vectors, size, k);
     int* centroids = initialize_centroids(vectors, size, k);
     bool centroids_updated = true;
     int count = 0;
     while(centroids_updated)
     {
         count++;
         if (count > max_iterations) break;
         assign_clusters(dimension, vectors, centroids, cluster_labels, size, k);
         centroids_updated = fix_centroids(dimension, vectors, centroids, cluster_labels, size, k);
     }

     print_clusters(cluster_labels, size);
     free(centroids);
     free(cluster_labels);
     return;
}

We were enumerating the differences between data mining and machine learning. Data Mining is generally used in conjunction with a database. Some of the algorithms included with models and predictions used with data mining fall in the following categories:
Classification algorithms - for finding similar groups based on discrete variables
Regression algorithms - for finding statistical correlations on continuous variables from attributes
Segmentation algorithms - for dividing into groups with similar properties
Association algorithms - for finding correlations between different attributes in a data set
Sequence Analysis Algorithms - for finding groups via paths in sequences

Some of the machine learning algorithms such as from MicrosoftML package includes:
fast linear  for binary classification or linear regression
one class SVM for anomaly detection
fast trees for regression
fast forests for churn detection and building multiple trees
neural net for binary and multi-class classification
logistic regression for classifying sentiments from feedback

Applications of machine learning are generally for :
1) making recommendations with collaborative filtering
2) discovering groups using clustering and unsupervised methods as opposed to neural networks, decision trees, support vector machines and bayesian filtering which are supervised learning methods
3) searching and ranking as used with pagerank for web pages
4) text document filtering
5) modeling decision trees and
6) for evolving intelligence such as with genetic programming and elimination of weakness

#codingexercise
Get cosine similarity between two vectors

double get_cosine_distance(int dimension, double* vec1, double* vec2)
{
    double distance = 0;
    double magnitude = 0;
    int i, j;
    for (i = 0; i < dimension; i++) distance += vec1[i] * vec2[i];
    for (i = 0; i < dimension; i++) magnitude += vec1[i] * vec1[i];
    magnitude = sqrt(magnitude);
    distance /= magnitude;
    magnitude = 0;
    for (i = 0; i < dimension; i++) magnitude += vec2[i] * vec2[i];
    magnitude = sqrt(magnitude);
    distance /= magnitude;
    return distance;
}

#classifier
another way to do kmeans : cexamples/classifier.c
but unit-tests are missing -sigh

Yesterday we discussed virtualization that is helpful to visualize data. In fact visualization is an important functional area for software development and many tools are written and developed to find knowledge in vast sets of data.
Today we explore data visualization. This is what distinguishes Data Mining from machine learning.
While machine learning uses concepts such as supervised and unsupervised classifiers, it can be understood as a set of algorithms. Data Mining on the other hand uses those and other algorithms in conjunction with a database so that the data can be queried to yield the result set that summarizes the findings. These result sets can then be drawn on charts and represented on dashboards.
Yet data mining and machine learning are separate domains in themselves. Machine learning may find use with text analysis and images and other static data that is not represented in tables. Data Mining on the other than translates most data into something that can be stored in a database and this has worked well for organizations that want to safeguard their data. Moreover, we can view the difference as top down and bottoms up view as well. For example, when we use statistics for building a regression model, we are binding different parameters together to mean something together and tuning it with experimental data. An unsupervised machine learning algorithm on the other hand builds a decision tree classifier based on the data as it is made available.  The output from a machine learning algorithm may be input for a data mining process. Some of the machine learning algorithms are forms of batch processing while data mining techniques may be applied in a streaming manner.
Both data mining and machine learning have been domain specific such as in finance, retail or telecommunications industry These tools integrate the domain specific knowledge with data analysis techniques to answer usually very specific queries.
Tools are evaluated on data types, system issues, data sources, data mining functions, coupling with a database or data warehouse, scalability, visualization and user interface.  Among these visual data mining is popular for its designer style user interface that renders data, results and process in a graphical and usually interactive presentation.
Visualization tools such as graphana stack for viewing elaborate charts and eye candies only require read permissions on the data as they execute queries on the result to fetch the data for making the charts.