Current status of training

1. University education

Established in 1997, up to now, the Posts and Telecommunications Institute of Technology has been training 16 general university training majors and 02 High-quality programs. The current university training majors of the Institute are divided into 03 groups of majors (1) Engineering and Technology group including Information Technology, Artificial Intelligence, Electronic Engineering, Communications and Electrical and Electronic Engineering Technology; (2) Business and Management group including Business Administration, Accounting, Marketing, E-commerce; and (3) Journalism and Communication group including Multimedia Technology, Multimedia Communications. Currently, the Institute has been organizing enrollment and training for 19 regular university courses; The Institute’s regular university training scale averages about 12,000 full-time university students (accounting for 85.7% of the Institute’s total training scale), of which the scale of full-time university students in the technical field is about 8,000 students (accounting for over 65% of the full-time university training scale).

The Institute has also trained human resources for society in 19 university courses with a number of over 15,000 workers. In 2016, the Institute conducted a survey of graduates. The drafting team designed a survey form, organized a survey of the employment of graduates at the Institute from businesses to have an independent information channel on the employment results of students with the survey results showing that 93.10% of graduates have jobs. In 2016 and 2017, the Institute conducted a survey on the employment of graduates through an online survey using a questionnaire on Google sent via email, Facebook and website, with 23.39% of students and 57.61% of students of newly graduated courses participating in the survey. According to the survey results, the number of students who had jobs immediately after graduation accounted for a large proportion of 68.3%, showing that many students of the Institute were proactive in finding and orienting their careers while still studying at school.

Also according to the survey, the rate of students who had jobs in their field of study after one year of graduation was 89.5%. In addition, nearly 15% of students after graduating from university and college continued to study to improve their qualifications at the Institute or study abroad. University training activities of the Posts and Telecommunications Institute of Technology meet the requirements of society and businesses, actively contributing to the development of the Information and Communications industry and the cause of industrialization and modernization of the country.

2. Short-term training

Lecturers of the Faculty of Information Technology 1 have actively cooperated with the Post and Telecommunications Training Center 1 and large enterprises such as Samsung and Naver to develop programs and conduct many training courses both in person and online, providing basic and in-depth training on machine learning and artificial intelligence for students of all faculties at the Institute. Some typical activities in training and fostering artificial intelligence that have been implemented include:

– Developing electronic lectures and online courses on artificial intelligence under the sponsorship of Naver Group.

– Developing and teaching direct courses on artificial intelligence for students under the sponsorship of Samsung Group.

– Participating in developing programs and conducting basic training courses on artificial intelligence for partners of the Institute.

3. Training program

3.1. General knowledge block

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3.2. Basic knowledge of major groups

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CMU: Focusing on Mathematics subjects closely related to AI such as: Mathematical Foundations for Computer Science; Integration and Approximation; Matrices and Linear Transformations; Calculus in Three Dimensions; Probability Theory for Computer Scientists. These subjects are usually taught by CMU in the first year or the first semester of the second year.

Edinburgh: Focusing on three Mathematics subjects such as: Introduction to Linear Algebra, Calculus and its Applications, Discrete Mathematics and Probability.

3.3. Professional education knowledge block

3.3.1. Basic major knowledge

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  • CMU: Focuses on basic subjects used to introduce basic knowledge of AI/ML such as: Concepts in Artificial Intelligence, Artificial Intelligence: Representation and Problem Solving, Introduction to Machine Learning; on Techniques and programming languages ​​such as: Principles of Functional Programming, Parallel and Sequential Data Structures and Algorithms; and computer systems such as: Introduction to Computer Systems
  • Edinburgh: The program includes basic subjects such as: Informatics 1 – Object Oriented Programming, Informatics 2 – Software Engineering and Professional Practice
  • Stanford: The program includes basic subjects such as Machine Learning (Applied), Artificial Intelligence: Principles and Techniques
  • ACM Data Science Curriculum: The recommended program has basic subjects in knowledge areas such as: Computing and Computer Fundamentals with subjects: Basic Computer Architecture, Storage System Fundamentals, Operating System Basics…; or Programming, Data Structures, and Algorithms with subjects: -Algorithmic Thinking & Problem Solving, Programming, Data Structures… The field of knowledge about AI is also mentioned in this document

3.3.2. Major knowledge

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3.3.3. Internship and Graduation Project: 12 credits (Graduation Internship – 6 credits and Graduation Project or Alternative Course – 6 credits)

  • CMU: The program includes specialized subjects in three departments (1) Cognition and Action Cluster with subjects such as: Neural Computation; Autonomous Agents; Cognitive Robotics: The Future of Robot Toys; Planning Techniques for Robotics; Mobile Robot Algorithms Laboratory; Robot Kinematics and Dynamics; (2) Machine Learning Cluster with subjects such as: Deep Reinforcement Learning & Control, Machine Learning with Large Datasets (Undergraduate), Deep Learning Systems: Algorithms and Implementation, Intermediate Deep Learning, Machine Learning for Structured Data, Foundations of Learning, Game Theory, and Their Connections, Machine Learning for Text and Graph-based Mining, Introduction to Deep Learning, Advanced Methods for Data Analysis); (3) Perception and Language Cluster with subjects such as: Natural Language Processing, Search Engines, Speech Processing, Computational Perception, Computational Photography, Computer Vision
  • Edinburgh: The program includes specialized subjects on AI such as Computational Cognitive Science, Foundations of Natural Language Processing, Introduction to Mobile Robotics, Machine Learning, Automated Reasoning, Speech Processing, and other specialized subjects such as: Algorithms and Data Structures, Software Testing, Introduction to Theoretical Computer Science, Elements of Programming Languages, Software Design and Modelling, Compiling Techniques, Computer Security… Software Design and Modeling, Compilation Engineering, Computer Security)
  • Stanford:The program includes many advanced subjects, spanning across AI areas such as: Computer Vision, NLP, Reinforcement Learning, Robotics…
  • ACM Data Science Curriculum: The program recommends two specialized knowledge areas that are suitable for the two current departments of the faculty such as (1) Machine Learning with subjects on ML-General, ML-Supervised Learning, ML-Unsupervised Learning, ML-Mixed Methods, ML-Deep Learning and Data Mining with subjects on Proximity Measurement, DM-Data Preparation, DM-Information Extraction, DM-Cluster Analysis, DM-Classification and Regression, DM-Pattern Mining, DM-Outlier Detection, DM-Time Series Data, DM-Mining Web Data, DM-Information Retrieval