Summary
In this chapter, we learned the basic concepts of recommender systems, starting with the definition of these systems and then understanding how the problem is approached. We analyzed the different types of recommender systems: CF, content-based filtering, and hybrid recommender systems.
Next, we saw how to use similarities in the data to identify possible fraudulent uses of credit cards. To do this, we trained a model based on the nearest neighbor algorithm but using a modified version of the traditional k-NN algorithm, where neighbors are given varying weights during the prediction or classification process.
Then, we saw how to implement a NIDS based on ensemble methods in MATLAB. Specifically, we adopted an AdaBoost algorithm to identify intrusions in a LAN network.
Finally, we introduced the techniques of deploying machine learning models regarding model compression. We analyzed the most popular model compression techniques, including pruning, quantization, knowledge...
