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

Scaler Neovarsity

Tuition costs:

5,09,000 INR

Area:

Computer Science

Duration:

18 months

Language:

English

Mode:

Fully Online

Description:

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.



Degree ILOs:

Degree structure

In total, Master of Science in Computer Science: Artificial Intelligence and Machine Learning is 2250 hours to complete; the programme is structured with tiers, which are described below.

Tier 1: Foundational Modules

375 required hours

Learners should complete 15 ECTS (3 courses) from Tier 1.

Relational Databases

Relational Databases

125h

Data Structures

Data Structures

125h

Design and Analysis of Algorithms

Design and Analysis of Algorithms

125h

Introduction to Computer Programming: Part 1

Introduction to Computer Programming: Part 1

125h

Introduction to Problem-Solving Techniques: Part 1

Introduction to Problem-Solving Techniques: Part 1

125h


Tier 2: Specialization in Artificial Intelligence and Machine Learning

1125 required hours

Learners should complete 45 ECTS (9 courses) from Tier 2

Productionization of Machine Learning (ML) systems

Productionization of Machine Learning (ML) systems

125h

Deep Learning for Computer Vision

Deep Learning for Computer Vision

125h

Deep Learning for Natural Language Processing (NLP)

Deep Learning for Natural Language Processing (NLP)

125h

Advanced Machine Learning

Advanced Machine Learning

250h

Distributed Machine Learning

Distributed Machine Learning

125h

Introduction to Deep Learning

Introduction to Deep Learning

125h

Introduction to Machine Learning

Introduction to Machine Learning

250h

Applied Statistics

Applied Statistics

125h

High Dimensional Data Analysis

High Dimensional Data Analysis

125h

Numerical Programming in Python

Numerical Programming in Python

125h

System Design

System Design

125h

DevOps

DevOps

125h


Tier 3: Electives

500 required hours

Learners should complete 20 ECTS (4 courses) courses

System Design

System Design

125h

Statistical Programming

Statistical Programming

125h

Introduction to Problem-Solving Techniques: Part 2

Introduction to Problem-Solving Techniques: Part 2

125h

Introduction to Computer Programming: Part 2

Introduction to Computer Programming: Part 2

125h

Front End UI/UX Development

Front End UI/UX Development

125h

JavaScript

JavaScript

125h

Front End Development

Front End Development

125h

Back End Development

Back End Development

125h

Foundations of Cloud Computing

Foundations of Cloud Computing

Advanced Back End Development

Advanced Back End Development

125h

Distributed Cloud Computing

Distributed Cloud Computing

NoSQL Cloud Datastores

NoSQL Cloud Datastores

Design Patterns

Design Patterns

125h

Advanced Algorithms

Advanced Algorithms

125h

Computer Systems and Their Fundamentals

Computer Systems and Their Fundamentals

125h

Low-Level Design and Design Patterns

Low-Level Design and Design Patterns

Practical Software Engineering

Practical Software Engineering

125h

Distributed Systems with High-Level System Design

Distributed Systems with High-Level System Design

Power BI for Data Analysis and Exploration

Power BI for Data Analysis and Exploration

Spreadsheets for Data Understanding

Spreadsheets for Data Understanding

Advanced Python Programming

Advanced Python Programming

Foundations of Machine Learning

Foundations of Machine Learning

125h

Business Case Studies

Business Case Studies

Studies in Data Science and Data Analytics

Studies in Data Science and Data Analytics

SQL for Data Analytics

SQL for Data Analytics

125h

Product Analytics

Product Analytics

125h

Data Visualisation Tools

Data Visualisation Tools

125h


Tier 3: Capstone

250 required hours

Learners should a complete 10 ECTS (1 courses) Capstone project.

Applied Computer Science Project

Applied Computer Science Project