MS in Computer Science: Artificial Intelligence and Machine Learning 

Specialisation track

Distributed Machine Learning

Data Warehousing with Hive
Apache Spark using Python
Distributed ML Training
Big Data Fundamentals
Getting Started with Big Data
Communication & Data Consistency
Hadoop Commands
Hive Functions and Operators
Machine learning with Spark ML
Feature Engineering & Prediction with Spark ML
Data Parallelism & Model Parallelism
Scalability & Fault-tolerance

productionization of ML Systems

Deploy ML Model on Scale
Machine Learning System Architecture
Build Classification Model
Create Rest API with Flask
Introduction to Docker & Kubernetes
Packaging the ML Model for Production
Getting Started with Deployment Platforms
Deploying the ML API with containers
Build Web app using Streamlit
Getting started with Apache AirFlow

Introduction to Deep Learning

Neural Networks & Deep Learning
Improving Deep Neural Networks
Structuring ML Projects
Getting Started with TensorFlow & Keras
Tensorflow & Keras Implementation
Shallow Neural Networks
Deep Neural Networks
Practical Aspects of Deep Learning
Optimization Techniques in Deep Learning
Structuring Machine Learning Project Foundation

Deep Learning for Computer Vision

Computer Vision Fundamentals
Convolutional Neural Networks
Advanced Algorithms in CNN
Introduction to Computer Vision & OpenCV
Components of Computer Vision
Introduction to CNN
Image Data Modelling
Transfer Learning & Hugging face
Convolutional Models Architecture
Object Detection Architectures

Deep Learning for NLP

NLP with Classification & Vector Spaces
Additional NLP Algorithms
Components of NLP
Text Data Modelling with Spacy
Topic Modelling
Sentiment Analysis with Logistic Regression
Sentiment Analysis with Naïve Bayes
Vector Spaces
Probabilistic Models
Sequence Models

Advanced Machine Learning

Handling Anomalies
Advanced Clustering Algorithms
Market Basket Analysis
Advanced Time Series Analysis
Customized Models for User Preferences
Handling data for Outlier events
Modelling for Outlier Events - Case Study
Advanced Clustering Techniques
Clustering Analysis - Case Study
Introduction to Market Basket Analysis
Data Preparation & Association Rule Mining
Evaluation Metrics & Vizualization
Movie Recommendation - Case Study

Model Selection and Rationale
Model Architecture & System Design
Loss Function and Optimization Strategy
Training and Validation
Model Interpretability and Explainability
Model Compression Techniques

Model Evaluation 

Performance Metrics
Comparative Analysis with Existing Models
Real-World Performance Evaluation
Model Robustness and Generalization

Productionization & Testing

Model Deployment
API Design and Integration
Testing & Performance
User Interface Design
System Performance & Scalability

Conclusion & Future Work

Summary of Findings
Evaluation of the Project
Limitations and Challenges
Future Directions and Recommendations