MS Artificial Intelligence and Machine Learning

This Artificial Intelligence and Machine Learning syllabus, developed by esteemed experts from Walsh College and Great Lakes, offers a comprehensive insight into AI and Machine Learning. The Machine Learning and Artificial Intelligence Masters in the USA is strategically structured, starting with the basics and advancing to compound, applied methodologies, ensuring learners acquire profound understanding and practical skills in this evolving field.

Year 1

Foundations

PYTHON FOR AI AND MACHINE LEARNING  

This module is designed to acquire a robust understanding of Python programming, particularly its application to AI and ML. Learners will be proficient in the fundamental and advanced concepts of AI and ML, capable of applying Python programming to navigate and innovate within these technological domains.

APPLIED STATISTICS  

This module explores the pivotal terms and concepts intrinsic to Exploratory Data Analysis and Machine Learning. It is structured to guide learners through the foundational elements of statistical analysis to the advanced methodologies employed in data-driven decision-making processes.

Machine Learning

SUPERVISED LEARNING 

This module is crafted to understand the essential algorithms, functionalities, and application scopes of Supervised Machine Learning. It is strategically structured to guide learners through comprehensive foundational algorithms to the advanced workings and applications in Supervised ML - Regression and Classification.

UNSUPERVISED LEARNING 

This module is designed to help learners learn the fundamental algorithms, operational mechanics, and application scopes intrinsic to Unsupervised Machine Learning. It is structured to guide learners through the workings and applications, with a specialized focus on Clustering and Dimensionality Reduction within the Unsupervised ML.

ENSEMBLE TECHNIQUES  

This module is developed to guide learners through the intricate Ensemble Techniques within Machine Learning. It is systematically structured to guide learners through the limitations of supervised standalone models and introduces them to a selection of techniques, notably Ensemble Techniques, designed to minimize complexities.

FEATURIZATION, MODEL SELECTION AND TUNING  

This module is designed to impart a thorough understanding of various concepts crucial for developing functional ML models in learners, including but not limited to, model selection and tuning, evaluating model performance, and exploring different regularization methods.

INTRODUCTION TO SQL  

This module introduces learners to the fundamental and advanced concepts in SQL programming. It is structured to guide them through a comprehensive exploration of Database Management Systems (DBMS), Normalization, and Joins, ensuring a robust understanding and practical capability in utilizing SQL for database management and manipulation.

Artificial Intelligence

INTRODUCTION TO GENERATIVE AI AND PROMPT ENGINEERING  

The primary objective of this MS in Artificial Intelligence in US module is to impart learners with a solid understanding of the fundamental concepts and advanced techniques of generative AI, renowned for its innovative and creative applications, and to provide mastery over the practical applications of prompt engineering.

INTRODUCTION TO NEURAL NETWORKS AND DEEP LEARNING  

The module principally aims to enable learners to comprehend the rationale behind using Neural Networks, acquaint them with its components, and facilitate practical knowledge and skills in building and tuning Deep Neural Networks using TensorFlow and Keras.

COMPUTER VISION  

This module equips learners with the essential knowledge and skills to process and work with images for image classification using Neural Networks and understand the more advanced architecture of Convolutional Neural Networks.

NATURAL LANGUAGE PROCESSING 

This module imparts learners the essential knowledge and skills to work with NLP using Python and traditional Machine Learning methods. It provides a deeper understanding of the advanced realm of Sequential Models and cutting-edge language models.

SELF-PACED MODULE: DEMYSTIFYING CHATGPT AND APPLICATIONS 

The module helps learners thoroughly understand ChatGPT, its functionality, and its applications. It introduces them to the concept of prompt engineering and how it can be utilized to fine-tune outputs for specific use cases.

SELF-PACED MODULE: CHATGPT - THE DEVELOPMENT STACK  

This module provides the learners with a robust understanding of the mathematical fundamentals of generative AI. It provides a comprehensive overview of transformer models and their utilization in generative AI, particularly in natural language.

Capstone Project

This module ensures learners acquire theoretical knowledge and practical Python, AI, and ML skills. They will implement foundational concepts to advanced applications and real-time project work. Learners will be adeptly guided to apply the learned concepts and methodologies effectively in various practical and technological domains, ensuring proficient application of Python, AI, and ML principles and providing an MS in AI and Machine Learning course with a certificate.

 

Year 2

Introduction to Data Science

The module primarily provides learners with a thorough understanding of Data Science and its integral domains. It emphasizes the significance of data management and extracting actionable results, which is crucial for organizational success and sustainability.

 

Network Fundamentals

This module provides learners with a foundational introduction to networks, particularly networking concepts pertinent to an enterprise environment. It guides learners through the foundational theories and practical aspects of Networking Design, Security, Implementation, and Remote Connectivity, ensuring a comprehensive and practical understanding across these domains.

Operating Systems and Virtualization

The module primarily equips learners with the essential knowledge and skills to implement operating systems in an enterprise setting, focusing on setting up and configuring client and server operating systems in networked environments and providing critical network services.

Data Visualization and Predictive Modeling

The module exposes learners to various methods and techniques of visual representation, enabling them to extract meaningful information from raw data, and utilize statistical tools and visualizations to enhance data comprehension, communication, and decision-making

Mathematics of Artificial Intelligence and Machine Learning

The module introduces learners to the critical mathematical concepts utilized in AI and Deep Learning, with a focused exploration of Linear Algebra and Analytic Geometry, enabling them to comprehend and apply these mathematical principles effectively within AI contexts.

Data Storage Technologies

The module provides learners with a comprehensive understanding of the foundations of database storage technologies, encompassing various aspects such as Database Storage Architecture, Physical Data Storage, Transaction Management, Database Storage APIs, Data Warehousing, Governance, and Big Data Systems, with a particular focus on their application to Data Analytics.

Capstone Project

The primary objective of this module is to facilitate the integration and application of program learning through the development and implementation of a Capstone Project, and to enhance learners' knowledge and skills in AI and ML through practical application in a business setting during the Practicum course. It awards a certificate of completion for the Masters in Artificial Intelligence and Machine Learning in the USA.

Complementary Learning Modules

Prompt Engineering

Generative AI

Deep Learning