Following are the links to my talks from the SoCal Rock and Roll Code Camp in UCSD Extension Campus, San Diego.
Using ASP.NET MVC to build a blogging engine in 60 minutes or
less.
MVC is a framework methodology that divides an application's
implementation into three component roles: models, views, and controllers.
ASP.NET now has built-in support for MVC style development and this session is
an introduction to using this technique for building a sample application, a
blogging engine. This session will
elaborate on differences between traditional ASP.NET post-back style
development versus the routes and REST architecture based thinking around MVC.
ASPECT.NET – Aspect
Oriented Programming in .NET, an Introduction
Aspect Oriented Programming (AOP) deals with factorization
in code i.e. separation of common concerns,
specifically cross-cutting concerns, as an advance in modularization. AOSD has been a popular
trend in development for quite some time in other programming environments and
IDE’s however it’s scope and exposure is limited among .NET developers.
This session is focused on getting developers a
deeper understanding of what AOP is all about and how to use it in their
everyday development. Aspect.NET is a language-agnostic visual environment for
developing aspect-oriented applications for Microsoft.NET that was implemented
as an add-in to Microsoft Visual Studio.NET 2005. Using Aspect.NET, the user
can define and weave aspects and assess the results of the weaving in his or
her projects.
Collaborative
Filtering 101 – An Introduction with SQL Server 2008 BI
Collaborative Filtering (CF) is defined as profiling or
classification of information based on specific entity relationships i.e. making
automatic predictions (filtering) about the interests of a user by collecting
likelihood information from many users (collaborating). The underlying
assumption of CF approach is that those who agreed in the past tend to agree
again in the future. For example, a collaborative filtering or recommendation system for music tastes could make
predictions about which music a user should like given a partial list of that
user's tastes (likes or dislikes).
In this session, we will discuss collaborative filtering
algorithms and applications in the current e-commerce systems. A wide array of
topics such as market basket analysis, association trees, singular value decomposition (SVD), naïve
Bayesian classification will be briefly discussed along with the implementation
of these algorithms in sites like Netflix, Amazon and digg / (google pagerank).
In the second half of the talk, attendees will get to see the step by step
implementation of a small scale recommender system using SQL Server 2008
business intelligence studio and C#.