Accreditation:
EQF7
MaltaSwitzerlandWisconsinCaliforniaWashington
Workload:
2250 hours | 90 ECTS
Tuition cost:
5,09,000 INR

Master of Science in Computer Science: Artificial Intelligence and Machine Learning

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Kind
Degree
Area
Computer & Mathematical Science
Mode
Fully Online
Language
English
Student education requirement
Undergraduate (Bachelor’s)
Standard length
18 months
Standard delivery length
18 months
Certificates
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\ Overview

The course teaches students comprehensive and specialised subjects in computer science; it teaches students cutting-edge engineering skills to solve real-world problems using computational thinking and tools. Most of this program is the case (or) project-based where students learn by solving real-world problems end to end. This program has core courses that focus on computational thinking and problem solving from first principles. The core courses are followed by specialization courses that teach various aspects of building real-world systems. This is followed by more advanced courses that focus on research-level topics, which cover state-of-the-art methods. The program also has a capstone project at the end, wherein students can either work on building end-to-end solutions to real-world problems (or) work on a research topic. The program also focuses on teaching the students the “ability to learn” so that they can be lifelong learners constantly upgrading their skills. Students can choose from a spectrum of courses to specialize in a specific sub-area of Computer Science like Artificial Intelligence and Machine Learning, Cloud and Full Stack Development, etc.

Target Audience

-  Ages 19-30, 31-65, 65+
  • Target Group

    • This course is designed for individuals who wish to enhance their knowledge of computer science and its various applications used in different fields of employment. It is designed for those that will have responsibility for planning, organizing, and directing technological operations. In all cases, the target group should be prepared to pursue substantial academic studies. Students must qualify for the course of study by entrance application. A prior computer science degree is not required; however the course does assume technical aptitude; and it targets students with finance, engineering, or STEM training or professional experience.

  • Mode of attendance

    • Online/Blended Learning

  • Structure of the programme - Please note that this structure may be subject to change based on faculty expertise and evolving academic best practices. This flexibility ensures we can provide the most up-to-date and effective learning experience for our students.The Master of Science in Computer Science combines asynchronous components (lecture videos, readings, and assignments) and synchronous meetings attended by students and a teacher during a video call. Asynchronous components support the schedule of students from diverse work-life situations, and synchronous meetings provide accountability and motivation for students. Students have direct access to their teacher and their peers at all times through the use of direct message and group chat; teachers are also able to initiate voice and video calls with students outside the regularly scheduled synchronous sessions. Modules are offered continuously on a publicly advertised schedule consisting of cohort sequences designed to accommodate adult students at different paces. Although there are few formal prerequisites identified throughout the programme, enrollment in courses depends on advisement from Woolf faculty and staff.The degree has 3 tiers: The first tier is required for all students, who must take 15 ECTS. In the second tier, students must select 45 ECTS from elective tiers. Tier Three may be completed in two different ways: a) by completing a 30ECTS Advanced Applied Computer Science capstone project, or b) by completing a 10 ECTS Applied Computer Science project and 20 ECTS of electives from the program.

  • Grading System

    • Scale: 0-100 points

    • Components: 60% of the mark derives from the average of the assignments, and 40% of the mark derives from the cumulative examination

    • Passing requirement: minimum of 60% overall

  • Dates of Next Intake

    • Rolling admission

  • Pass rates

    • 2023 pass rates will be publicised in the next cycle, contingent upon ensuring sufficient student data for anonymization.

  • Identity Malta’s VISA requirement for third country nationals: https://www.identitymalta.com/unit/central-visa-unit/

    • Passing requirement: minimum of 60% overall

  • Dates of Next Intake

    • Rolling admission

  • Pass rates

375 hours | 15 ECTS

Tier 1: Foundational Modules

125 hours | 5 ECTS

Productionization of Machine Learning Systems

125 hours | 5 ECTS

Mathematics for Computer Science

125 hours | 5 ECTS

Introduction to Problem-Solving Techniques: Part 1

125 hours | 5 ECTS

Introduction to Computer Programming: Part 1

125 hours | 5 ECTS

Data Structures

125 hours | 5 ECTS

Design and Analysis of Algorithms

125 hours | 5 ECTS

Relational Databases

1125 hours | 45 ECTS

Tier 2: Specialization in Artificial Intelligence and Machine Learning

125 hours | 5 ECTS

Productionization of Machine Learning Systems

125 hours | 5 ECTS

Statistical Programming

125 hours | 5 ECTS

Product Analytics

125 hours | 5 ECTS

DevOps

125 hours | 5 ECTS

High Dimensional Data Analysis

125 hours | 5 ECTS

Numerical Programming in Python

125 hours | 5 ECTS

System Design

125 hours | 5 ECTS

Introduction to Deep Learning

125 hours | 5 ECTS

Advanced Machine Learning

125 hours | 5 ECTS

Deep Learning for Computer Vision

125 hours | 5 ECTS

Applied Statistics

125 hours | 5 ECTS

Introduction to Machine Learning

125 hours | 5 ECTS

Distributed Machine Learning

125 hours | 5 ECTS

Deep Learning for Natural Language Processing

750 hours | 30 ECTS

Tier 3: Capstone

125 hours | 5 ECTS

Introduction to Computer Programming: Part 2

125 hours | 5 ECTS

Introduction to Deep Learning

125 hours | 5 ECTS

Introduction to Machine Learning

125 hours | 5 ECTS

JavaScript

125 hours | 5 ECTS

Low-Level Design and Design Patterns

125 hours | 5 ECTS

Numerical Programming in Python

125 hours | 5 ECTS

Distributed Machine Learning

125 hours | 5 ECTS

Practical Software Engineering

125 hours | 5 ECTS

Product Management for Software Engineers

125 hours | 5 ECTS

Productionization of Machine Learning Systems

125 hours | 5 ECTS

System Design

125 hours | 5 ECTS

Applied Statistics

125 hours | 5 ECTS

Back End Development

125 hours | 5 ECTS

Computer Systems and Their Fundamentals

250 hours | 10 ECTS

Data Engineering

125 hours | 5 ECTS

Data Structures

125 hours | 5 ECTS

Deep Learning for Natural Language Processing

125 hours | 5 ECTS

Deep Learning for Computer Vision

125 hours | 5 ECTS

Design and Analysis of Algorithms

125 hours | 5 ECTS

Design Patterns

125 hours | 5 ECTS

DevOps

125 hours | 5 ECTS

Distributed Cloud Computing

125 hours | 5 ECTS

Statistical Programming

125 hours | 5 ECTS

Product Analytics

125 hours | 5 ECTS

Foundations of Machine Learning

125 hours | 5 ECTS

SQL for Data Analytics

125 hours | 5 ECTS

Introduction to Problem-Solving Techniques: Part 2

125 hours | 5 ECTS

Data Visualisation Tools

125 hours | 5 ECTS

Advanced Algorithms

125 hours | 5 ECTS

Advanced Back End Development

125 hours | 5 ECTS

Advanced Machine Learning

125 hours | 5 ECTS

Front End Development

125 hours | 5 ECTS

Front End UI/UX Development

125 hours | 5 ECTS

High Dimensional Data Analysis

250 hours | 10 ECTS

Applied Computer Science Project

625 hours | 25 ECTS

Specialization certificate in Backend Engineering

125 hours | 5 ECTS

Low-Level Design and Design Patterns

250 hours | 10 ECTS

Applied Computer Science Project

125 hours | 5 ECTS

Computer Systems and Their Fundamentals

125 hours | 5 ECTS

Practical Software Engineering

625 hours | 25 ECTS

Specialization certificate in Full-Stack Engineering

125 hours | 5 ECTS

Front End Development

125 hours | 5 ECTS

JavaScript

125 hours | 5 ECTS

Front End UI/UX Development

250 hours | 10 ECTS

Applied Computer Science Project

500 hours | 20 ECTS

Specialization certificate in Software Engineering

125 hours | 5 ECTS

Product Management for Software Engineers

250 hours | 10 ECTS

Data Engineering

125 hours | 5 ECTS

System Design

500 hours | 20 ECTS

Specialization certificate in Data Science and Data Analytics

125 hours | 5 ECTS

Product Analytics

125 hours | 5 ECTS

Statistical Programming

125 hours | 5 ECTS

Numerical Programming in Python

125 hours | 5 ECTS

Applied Statistics

\ Intended learning outcomes

Knowledge
Knowledge acquired by the learner at the end of the course:
- Define and explain core concepts in Artificial Intelligence, such as natural language processing, deep learning, and reinforcement learning - Analyze and critically evaluate the strengths and weaknesses of different machine learning algorithms - Compare and contrast various search techniques used in Artificial Intelligence
Skills
Skills acquired by the learner at the end of the course:
- Implement and apply machine learning algorithms in Python to solve real- world problems - Design and develop a simple neural network architecture for image recognition - Troubleshoot and debug errors encountered while working with machine learning models
Competencies
Competencies acquired by the learner at the end of the course:
- Formulate and solve a research question related to Artificial Intelligence or Machine Learning, and design a methodology to investigate it - Communicate and advocate the findings of the research project to a technical and non-technical audience - Adapt and innovate existing machine learning techniques to solve novel problems in different domains

Are you ready to take the next step towards your academic success?

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