Topics

Program Overview
Degree Offerings
Admission Requirements
Curriculum Structures
Courses
Note

Post-Graduate Programs in Artificial Intelligence and Machine Learning


Program Overview:

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference. Today, artificial intelligence is at the heart of many technologies we use. Organizations are incorporating AI techniques to automate tasks, accelerate decision-making, and impart human-like capabilities to machines.

Machine learning acts as a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data and then apply that learning to make increasingly better decisions. By studying and experimenting with machine learning, researchers and practitioners test the limits of how much they can improve the perception, cognition, and action of a computer system.

Though artificial intelligence and machine learning courses have been taught in this department for many years, to fully leverage the availability of these fields in our country, the introduction of a specialized graduate program is of prime importance. This will also have a huge impact on the 4th Industrial Revolution (4IR) in this country. Moreover, many of the SDG goals of Bangladesh, such as SDG 3: Healthy Lives and Well-being and SDG 11: Sustainable Cities and Communities, will be benefitted.

In light of the aforementioned scenario, we propose Master’s and Ph.D. programs in Artificial Intelligence and Machine Learning from the Department of Computer Science and Engineering (CSE) at Bangladesh University of Engineering and Technology (BUET).

The prospective students of these programs are recommended to fulfill the following abilities:

  • ○ Strong mathematical background in Linear Algebra, Probability, and Optimization
  • ○ Basic (undergraduate level) Expertise in artificial intelligence and machine learning.

Degree Offerings

  • 1. Master of Science in Artificial Intelligence and Machine Learning (M. Sc. AI and ML)
    • Duration: 18 months
    • Course Structure: 18 Credit courses + 18 Credits Thesis
    • Mode: Thesis Based

  • 2. Doctor of Philosophy (Ph.D.) in Artificial Intelligence and Machine Learning
    • Duration: 3-5 years
    • Total Credit: 54 Credits

Admission Requirements:

For Master’s Degrees

The admission requirements for the Master’s programs are as follows:

  • 1. 4-year B.Sc. in Computer Science and Engineering (CSE) or Electrical and Electronic Engineering (EEE) or Computer Science (CS) or relevant fields.

For Ph.D. Degree

The admission requirement for the Ph.D. program is as follows:

  • 1. Master of Science in Artificial Intelligence and Machine Learning or Master of Science in Computer Science and Engineering (CSE) or Electrical and Electronic Engineering (EEE) or relevant fields.

Curriculum Structures:

1. For students satisfying Group A entry requirements:

Program Courses Thesis/ Project Credit Total Credit
Total Number of Courses (Credit) Number of Foundation Courses Minimum Number of Core Courses
M. Sc. (AI and ML) 6 Courses (18 Credit) 0 4 (at least one from Group 1) 18 Credit 36 Credit
Ph.D. 3 Courses (9 Credit) 0 2 45 Credit 54 Credit

Courses:

Core Courses - Group 1

  • 1. CSE 6501: Advanced Artificial Intelligence
  • 2. CSE 6512: Advanced Machine Learning

Core Courses - Group 2

  • 1. CSE6505: Speech Recognition
  • 2. CSE6506: Data Mining
  • 3. CSE6507: Machine Translation
  • 4. CSE6508: Evolutionary Algorithms
  • 5. CSE6509: Text-to-Speech Synthesis
  • 6. CSE6510: Natural Language Processing
  • 7. CSE6511: Computer Vision
  • 8. CSE6701: Neural Networks
  • 9. CSE6704: Fuzzy Systems
  • 10. CSE6705: Meta-Heuristics
  • 11. CSE6708: Semantic Web
  • 12. CSE6709: Deep Learning
  • 13. CSE6710: Reinforcement Learning

Note:

  • 1. The remaining required courses (i.e., courses other than the Foundation courses and Core courses) can be taken from any PG courses.
  • The thesis/project must be on a topic relevant to the program area.

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