FIT5047 – Intelligent Systems Week 7

This week’s lecture delved further into probabilistic systems, specifically Bayesian networks. We explored the rationale behind the explosion in probabilistic systems in the past 15 years, namely the computational shortcuts achieved via the markov property and real world usefulness of  refining probabilities as known information increases. We got much more detail about Bayesian network this week, including the components: Random Variables [nodes, represented as binary, ordered, integral or continuous states] Node links Conditional Probability table [Quantifying the affect of parent.. Read More

FIT5059 – Adv. Prog. for DB Applications Week 7

Continuation of previous lecture focusing of multiple forms vs multiple canvasses. This is a major concern for distributed development, an area I don’t think it is a strong point for Oracle forms. The source code from tutorial 7:

 

FIT5167 – Natural Computation Week 7

Week 7 introduced Genetic Algorithms, who’s effectiveness is somewhat disputed. In any case, these algorithms are quite interesting in their balance between a kind of hill climbing (fitness function) and stochastic methods (cross over, mutation). The lecture gave the natural basis for these algorithms and defined the key components: Chromosome (ie 101101101) Reproduction (ie crossover/roulette/tournament) Mutation Fitness functions The second half of the lecture was dedicated to assignment and unit test revision.