Classes of machine-learning techniques

Workflow

The Five Tribes of Machine Learning

Content Metrics

CHARACTERIZATION OF MEME PROPAGATION AND PERSISTENCE

Cultural Evolution and Memetics

• Culture: the attitudes, beliefs, and behaviors that, for a certain group, define their general way of life and that they have taken over from others.
• Cultural evolution: the development of culture over time, as conceptualized through the mechanisms of variation and natural selection of cultural elements
• Replicator: an information pattern that is able to make copies of itself, typically with the help of another system. Examples are genes, memes, and (computer) viruses.
• Meme: a cultural replicator; a unit of imitation or communication.
• Memeplex (or meme complex): a collection of mutually supporting memes, which tend to replicate together
• Memetics: the theoretical and empirical science that studies the replication, spread and evolution of memes
• Fitness: the overall success rate of a replicator, as determined by its degree of adaptation to its environment, and the three requirements of longevity, fecundity and copying- fidelity.
• Longevity: the duration that an individual replicator survives.
• Fecundity: the speed of reproduction of a replicator, as measured by the number of copies made per time unit
• Copying-fidelity: the degree to which a replicator is accurately reproduced.
• Vertical transmission: transmission of traits (memes or genes) from parents to offspring 
• Horizontal transmission: transmission of traits between individuals of the same generation
• Memotype: a meme in the form of information held in an individual’s memory. Mediotype: a meme as expressed in an external medium, such as a text, an artefact, a song, or a behavior.
• Sociotype: the group or community of individuals who hold a particular meme in their memory.

Look again!

Black Sabbath - Children Of The Grave/Embryo

Recommender Systems Function

• Increase the number of items sold.
• Sell more diverse items.
• Increase the user satisfaction.
• Increase user fidelity.
• Better understand what the user wants.

Main steps and methods in a Data Mining problem

Parallel and sequential ensembles recommender systems

The project risk management process

Business systems exist in a field of interrelated forces, which naturally push back on attempts to change the system.

Ciclo Vicioso em Multi Projetos

Single and multilayer neural networks

Educational Recommender Systems

The conceptual goals of various recommender systems

Recommender Systems

Recommender Systems

The two primary models are as follows:

1. Prediction version of problem: The first approach is to predict the rating value for a
user-item combination. It is assumed that training data is available, indicating user
preferences for items. For m users and n items, this corresponds to an incomplete
m × n matrix, where the specified (or observed) values are used for training. The
missing (or unobserved) values are predicted using this training model. This problem
is also referred to as the matrix completion problem because we have an incompletely
specified matrix of values, and the remaining values are predicted by the learning
algorithm.

2. Ranking version of problem: In practice, it is not necessary to predict the ratings of
users for specific items in order to make recommendations to users. Rather, a merchant
may wish to recommend the top-k items for a particular user, or determine the top-k
users to target for a particular item. The determination of the top-k items is more
common than the determination of top-k users, although the methods in the two cases
are exactly analogous. Throughout this book, we will discuss only the determination of
the top-k items, because it is the more common setting. This problem is also referred
to as the top-k recommendation problem, and it is the ranking formulation of the
recommendation problem.