Machine learning: Classification FUNiX

ABOUT THE COURSE!

Machine learning classification is one of the most popular research area in the machine learning field. Today, you can see the applications of machine learning classification in many places like when you post an image to Facebook it can recognize your face and your friend’s faces, when you go on the Internet you probably have experience that there are many ads show to you which are based on your interest and what you had searched on Google before, you probably have heard that machine learning classification help predicting if a patient has a disease or not in the medical field, … 

This third course of the Machine Learning Program aims at providing learners with interesting topics of Machine Learning - Classification including common classification problems and algorithms. Through the course, you will be introduced widely used algorithms like Decision Tree, Random Forest, SVM or Neural Network. More importantly, apart from exploring these algorithms, you will have a chance to apply them to real-world datasets as well as evaluating classification models and using them in appropriate problems. 

To begin the course, let's take a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments/projects/quizzes you’ll need to complete to pass the course.

Main concepts are delivered through videos, demos and hands-on exercises.

COURSE INFORMATION

COURSE OBJECTIVES

After taking this course, the students should all be able to:

• Know what classification problems in machine learning are
• Have intuition and knowledge about Linear Classifiers & Logistic Regression model, how they are learned using gradient ascent and apply then to real word examples and datasets
• Have knowledge about overfitting and regularization in classification and how to prevent them
• Have intuition and knowledge about Decision Tree model and how the model is learned using greedy algorithm. Apply the model to real word examples and datasets
• Have knowledge about overfitting and regularization in classification and how to prevent them
• Have intuition and knowledge about Boosting model and how the model is learned. Apply the algorithm to real world examples and datasets
• Have intuition and knowledge about SVM, Naive Bayes, Random Forest, how they are learned and apply them to real word examples and datasets
• Have intuition and knowledge about Feed forward Neural Network, how the model is learned using gradient descent. Apply the model to real word examples and datasets
• Have intuition and knowledge about precision and recall. Know when to use them for different problems
• Have intuition and knowledge about methods for to huge datasets. Use them for real word examples.
• Apply all classification algorithms in the course to a real world problem and dataset

COURSE STRUCTURE

COURSE DESIGNERS

REVIEWERS & TESTER

 Program Reviewers

Assoc. Prof. Tu Minh Phuong Dean of IT Faculty Posts and Telecommunications Institute of Technology (PTIT)	Ph.D. Hoang Anh Minh R&D Manager, FPT Software Chief Scientist, LA Office	Ph.D. Le Hai Son       Machine Learning Expert       FPT Technology Innovation

MOOC MATERIALS

Below is the list of all free massive open online learning sources (MOOC) from Coursera used for this course by FUNiX: 

• Machine Learning: Classification offered by University of Washington
• Machine Learning offered by Stanford University