SIT - Shaggar Institute of Technology
Artificial Intelligence Background

Artificial Intelligence

Discover our cutting-edge curriculum and research opportunities in artificial intelligence.

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The Artificial Intelligence Department at Shaggar Institute of Technology (SIT) prepares students to build the intelligent systems shaping the future. Our program combines rigorous foundations in machine learning, deep learning, and data-driven reasoning with hands-on projects, ensuring graduates are equipped with both deep technical expertise and the critical problem-solving skills to apply AI responsibly.

With a strong emphasis on innovation, research, and responsible AI, students explore five key focus areas:

  • Machine Learning: Building models that learn from data to make predictions and decisions.

  • Deep Learning & Neural Networks: Designing the architectures behind modern AI breakthroughs.

  • Natural Language Processing & LLMs: Enabling machines to understand and generate human language.

  • Computer Vision: Teaching systems to perceive and interpret the visual world.

  • Intelligent Robotics & Autonomous Systems: Applying AI to perception, control, and decision-making in the physical world.

At SIT, students gain hands-on experience through research, industry partnerships, and real-world projects, ensuring they become innovators and leaders in Ethiopia's growing artificial intelligence sector and beyond.

Program Duration

Quarter-based system with three quarters per year, each spanning 12-13 weeks

Learning Approach

5E Instructional Model, an approach that revolves around five pivotal stages. Each stage plays a distinct role in the learning process.

Preparing the next generation of artificial intelligence professionals

Program Objectives

Our program prepares graduates to be leaders in artificial intelligence, developing innovative, responsible solutions while adhering to ethical standards and sustainability principles.

Apply machine learning and AI techniques to develop innovative solutions

Serve as ethical leaders in the field of artificial intelligence

Employ AI to address real societal and industrial challenges

Assume leadership roles in professional and community advancement

Pursue lifelong learning and adapt to rapid advances in AI

Expected Outcomes

Upon graduation, our students will demonstrate:

Design, train, and evaluate machine learning and deep learning models for real-world applications

Build natural language processing and computer vision systems

Apply AI to robotics, perception, and autonomous decision-making

Work with large-scale data and the computing infrastructure that powers modern AI

Uphold ethics, fairness, and responsibility in the design and deployment of AI systems

Solve complex problems with innovative, data-driven solutions

Work collaboratively in multidisciplinary teams and communicate technical concepts effectively

Adapt to emerging advances and contribute to Ethiopia's AI-driven transformation

Empowering future engineers with the knowledge and skills to transform tomorrow's technology landscape

Curriculum Overview

Graduation Requirements

  • 120 credit hours within four years
  • 94 credit hours of subject area classes and electives
  • Minimum GPA of 2.5
  • Final score of PASS is mandatory

Research Requirements

  • 20 credit hours of apprenticeship
  • 6 credit hours of research thesis with successful defense
  • Research at SIT's incubation or approved organizations

Core Foundations

  • Ethics in Technology
  • Programming Fundamentals I & II
  • Discrete Mathematics
  • Linear Algebra and Probability for AI
  • Data Structures and Algorithms
  • Database Systems
  • Introduction to Artificial Intelligence
  • Computer Systems and Architecture & Operating Systems
  • Technical Communication

Machine Learning & Deep Learning

  • Foundations of Machine Learning
  • Deep Learning and Neural Networks
  • Natural Language Processing & Large Language Models
  • Computer Vision and Image Understanding
  • Reinforcement Learning and Decision Making
  • AI Ethics and Responsible Innovation
  • Generative AI Systems

Intelligent Robotics & Autonomous Systems

  • Robotics Fundamentals
  • Intelligent Automation
  • Human-Robot Interaction
  • Autonomous Systems Design
  • AI for Robotics

Advanced Electives

  • Cloud Computing and AI Infrastructure
  • Big Data Analytics
  • Data Visualization and Communication
  • Advanced AI Algorithms
  • AI in Practice: Industry Applications
  • Multimodal AI Systems
  • Quantum Computing
  • Advanced Topics in AI

Shape the Future of Artificial Intelligence

Join our cutting-edge Artificial Intelligence program