EGCO 623

Course Prerequisites

• Basic Mathematics

• Basic Statistics

Course Description

Concepts of data mining, data warehouse, data and data preprocessing, measures of similarity and dissimilarity, visualization, knowledge discovery from database, machine learning concept, supervised and unsupervised learning, inductive learning, decision tree learning, artificial neural networks, reinforcement learning, evolutionary algorithms, evaluating the performance of a classifier

Materials

All materials were moved to https://classroom.google.com/c/NTIzOTY5MzY2OTAz?cjc=vrplh2h

• Syllabus

• Introduction to DM

• Exploring Data

• Data Preprocessing

• Association Analysis

• Cluster Analysis

• Visualization

• Introduction to Machine Learning

• Decision Tree, Random Forest, Bayes Learning, SVM

• Neural Network and Deep Learning

• Reinforcement Learning and Evolutionary Computation

• Ensemble Methods

• Model Evaluation

• Machine Learning Software

Grading

Grades will be assigned on the following basis:

• Midterm Exam 10 %

• Final Exam 10 %

• Assignments 30 %

• Project 10 %

• Attendance 40 %