Computational Intelligence Course

This course provides students with basic concepts of artificial intelligence and soft computing, several types of data input, data processing and transformation, feature vectors and feature engineering, comprehensive understanding of classification methods with supervised and unsupervised learning, and optimization methods with evolutionary algorithms, as well as data dimension reduction. Students are also able to apply these methods to case studies in the form of project assignments, analyze and evaluate the results of their application, and present the modeling results in a paper. This course will discuss several methods related to their respective uses. Dimensional reduction and data transformation methods studied include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Independent Component Analysis (ICA). Supervised learning includes Multi-Layer Perceptron (MLP), RBF, ANFIS, Support Vector Machine (SVM) while unsupervised learning includes a variety of clustering methods (K-Means, Hierarchical Clustering, DBSCAN). Then, the optimization methods used include evolutionary algorithms such as Genetic Algorithm (GA), Ant Colony (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC).

PLO-8
Mampu mengimplementasikan kebutuhan computing dengan mempertimbangkan berbagai metode/algoritma yang sesuai (COM-03)

PO-1
Memilih representasi pengetahuan dan mekanisme penalarannya.

PO-2
Memahami konsep dasar dan teori kecerdasan komputasional berikut konsep dan turunan algoritmanya.

PO-3
Menentukan pendekatan kecerdasan komputasional yang sesuai dengan masalah yang dihadapi.

PO-4
Menerapkan kecerdasan komputasional dengan mengimplementasikan algoritma secara pendekatan yang berbeda yaitu supervised, unsupervised, dan semi supervised sesuai dengan permasalahan yang dihadapi.

PO-5
Menganalisis implementasi algoritma secara secara pendekatan yang berbeda yaitu supervised, unsupervised, dan semi supervised.

PO-6
Mengevaluasi implementasi algoritma secara secara pendekatan yang berbeda yaitu supervised, unsupervised, dan semi supervised.