Skip to main content

Machine learning: Classification

Enrollment in this course is by invitation only

ABOUT THE COURSE!

Machine Learning Classification is one of the most popular research area in the machine learning field. Today, you can see the application of machine learning classification in many places. For example, when you post an image to Facebook, it can recognize your face and your friend’s faces; or when you go on the Internet, you probably have experience that there are many ads shown to you based on your interests and what you have searched on Google before. Further more, you have probably heard that machine learning classification can help predict if a patient has a disease or not.

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 code: MLP303x
Course name: Machine Learning: Classification
Credits: 3
Estimated Time: 6 weeks. Student should allocate at average of 2 hours/a day to complete the course.

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

Module 1: Understand classification problems

  • Lesson 1 - Understand classification problems

Module 2: Fundamental classification algorithms

  • Lesson 2 - Linear Classifier and Logistic Regression
  • Lesson 3 - Learning Linear Classifiers
  • Lesson 4 - Overfitting & Regularization in Logistic Regression
  • Lesson 5 - SVM
  • Lesson 6 - Naïve Bayes
  • Lesson 7 - Neural Networks: Representation
  • Lesson 8 - Neural Networks: Learning
  • Lesson 9 - Decision Tree
  • Lesson 10 - Overfitting in Decision Trees
  • Lesson 11 - Boosting
  • Lesson 12 - Random forest

Assignment 1 - Project - Default Risk Prediction

Module 3: Evaluation, handling huge dataset, machine learning system design

  • Lesson 13 - Precision and recall
  • Lesson 14 - Scaling to Huge Datasets & Online Learning
  • Lesson 15 - Machine Learning System Design

Assignment 2 - Project - Evaluation Metrics for Classifiers

DEVELOPMENT TEAM

COURSE DESIGNERS

Ph.D. Nguyen Van Vinh

  • Lecturer & core member of AI Lab, University of Technology, Vietnam National University (VNU)
  • AI expert & consultant for DPS, Fsoft
  • Ph.D. in Computer Science, Japan Advanced Institute of Science & Technology
  • Bachelor’s degree in IT, University of Science, VNU

B.A. Luu Truong Sinh



REVIEWERS & TESTER

Course Reviewer

 

Course Tester

 

Ph.D. Tran Tuan Anh

  • Lecturer at Ho Chi Minh National University - University of Science (HCMUS)
  • Ph.D of Computer Science, Chonam National University, Korea
  • M.Sc. Applied Mathematics, University of Orleans, France
    in AI & machine learning

     M.Sc. Nguyen Hai Nam

 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: 


Learning resources

In modern times, each subject has numerous relevant studying materials including printed and online books. FUNiX Way does not provide a specific learning resource but offers recommendation for students to choose the most appropriate source to them. In the process of studying from many different sources based on that personal choice, students will be timely connected to a mentor to respond to their questions. All the assessments including multiple choice questions, exercises, projects and oral exams are designed, developed and conducted by FUNiX.  

Learners are under no obligation to choose a fixed learning material. They are encouraged to actively find and study from any appropriate sources including printed textbooks, MOOCs or websites. Students are on their own responsibilities in using these learning sources and ensuring full compliance with the source owners’ policies; except for the case in which they have an official cooperation with FUNiX. For further support, feel free to contact FUNiX Academic Department for detailed instructions. 

Learning resources are recommended below. It should be noted that listing these learning sources does not necessarily imply that FUNiX has an official partnership with the source’s owner: CourseratutorialspointedX TrainingUdemyMachine Learning cơ bản, or Towards Data Science.


 Feedback channel

FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email program@funix.edu.vn