Agenda:
- An overview of AI applications in various areas.
- 5 fundamental questions, an ML system may try to answer.
- Defining a problem, to make it solvable with AI.
The slides have been presented on the CodeteCON LBN#2.
it exists and everybody does it ✓ availability of data ✓ a well described problem we don’t know the algorithm for, or an algorithmic approach is not performant enough and the general correctness is not necessary input output algorithm
containing some observations (i.e. images) and a limited set of categories, every observation belongs to, but only to one of them. In other words, for each observation there is only one category assigned.
related to classification algorithms. Such an algorithm chooses the most probable category for given observation. A majority of classification methods will require to provide so called training dataset containing many observations from all the categories. These examples are then used in order to find a generalization for each category, and these generalized categories may be in turn used for the further labeling the observation our model hasn’t seen before. car
have the dataset of some observations collected and we want to detect some anomalies. The underlying assumption is, there is some expected behaviour, and we want to detect any unusual pattern.
numerical value is also quite common. We no longer have a limited number of categories to ask for, but a continuous space that an output may come from. Commonly, we have a set of measurements given and try to find the pattern that will allow us to predict the value in previously unseen conditions.
example of such a problem. If we describe it in terms of numerical values, like temperature, humidity, etc., we can easily ask a question about their values in a particular point of time.
be solved with regression algorithms. Their purpose is to predict the numerical value, usually based on the historical values under different conditions.
observations we don’t know too much about. As we would like to have an overview of what is inside, understand it a little bit, we can ask a question if the dataset is organized in any way. The difference to the previous question is - we usually don’t have any labels assigned to the entries of our dataset.
problem is probably the IRIS dataset. It contains the examples of three different kinds of irises - each observation is described in terms of sepal and petal width and length.
help to answer this kind of questions. Such algorithms try to divide the dataset into groups in which the similarity of observations is higher than between two examples coming from two different groups - so called clusters.
model the ongoing process in which there are several small decisions to be taken. It is quite similar to the way our brains work - when we have a goal to achieve, there are usually many different ways to get there - some of which are more effective than the others.
such problems are video games. Typically, we have a limited set of possible actions at each point of time and need to decide what to do next in order to win the game. In this very moment we cannot say which action is the best possible one.
reinforcement learning algorithms is to learn from the experience, through trial-and-error approach. An ML system based on such algorithm is punished or rewarded for every performed action with a goal to maximize the overall reward.
or B? Classification ✓ Is this weird? Anomaly detection ✓ How much / how many? Regression ✓ How is this organized? Clustering ✓ What should I do next? Reinforcement learning
Broadway”, one of the top 10 challenges that data professionals have faced in the Past Year is: “The lack of clear question to be answering of a clear direction to go with the available data”.
to solve with Machine Learning? ✓ Did you try to use an algorithmic approach? Is there any way to solve your problem using it? ✓ Are you able to rephrase the issue to match one of the fundamental questions? Is there anyone who can do it? ✓ Do you have the data for training? ✓ Are you aware of the fact, there is a high risk of failure?
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