MS Artificial Intelligence and Machine Learning

The curriculum is designed by leading experts and practicing industry professionals at Walsh College and Great Lakes. It is specifically curated to cover a range of AI and Machine Learning skills, starting from the fundamentals and progressing to more complex, hands-on applications.

Year 1

Foundations

PYTHON FOR AI AND MACHINE LEARNING  

This course focuses on Python programming used for Artificial Intelligence and Machine  Learning. Learners will work on a high-level idea of Object-Oriented Programming and later  learn the essential vocabulary (keywords), grammar (syntax) and sentence formation (usable  code) of this language. This module will drive learners from introduction to AI and ML to the core  concepts using one of the most popular and advanced programming languages - Python. 

APPLIED STATISTICS  

Learn the terms and concepts vital to Exploratory Data Analysis and Machine Learning in  general. From the very basics of taking a simple average to the advanced process of finding  statistical evidence to confirm or deny conjectures and speculations, learners will focus on a  specific set of tools required to analyze and draw actionable insights from data. 

 

Machine Learning

SUPERVISED LEARNING 

Learn about Supervised ML algorithms, working of the algorithms and their scope of  application - Regression and Classification. 

UNSUPERVISED LEARNING 

Learn about Unsupervised Learning algorithms, working of these algorithms and their scope of  application - Clustering and Dimensionality Reduction. 

ENSEMBLE TECHNIQUES  

In this Machine Learning module, work on supervised standalone models’ shortcomings and learn  a few techniques, such as Ensemble techniques to overcome these shortcomings. 

FEATURIZATION, MODEL SELECTION AND TUNING  

Learn various concepts that will be useful in creating functional machine learning models like  model selection and tuning, model performance measures, ways of regularization, etc. 

INTRODUCTION TO SQL  

Know about SQL programming, such as DBMS, Normalization, Joins, etc.

Artificial Intelligence

INTRODUCTION TO GENERATIVE AI AND PROMPT ENGINEERING  

This module offers a comprehensive exploration of two critical aspects of Artificial Intelligence.  Through live sessions, learners will delve into the fundamental concepts and techniques of  generative AI, a field known for its innovation and creativity. Learners will also master the  practical applications of prompt engineering, including designing effective prompts, optimizing  results, and exploring various prompt engineering techniques. 

INTRODUCTION TO NEURAL NETWORKS AND DEEP LEARNING  

In this Artificial Intelligence module, learners understand the motive behind using the terms  Neural Network and look at the individual constituents of a Neural Network, installation of and  building familiarity with TensorFlow library, appreciate the simplicity of Keras and build a deep  neural network model for a classification problem using Keras. They also learn how to tune a  Deep Neural Network.  

COMPUTER VISION  

In this Computer Vision module, learn how to process and work with images for image  classification using Neural Networks. Going beyond plain Neural Networks, learners will also  focus on a more advanced architecture - Convolutional Neural Networks. 

10 

NATURAL LANGUAGE PROCESSING 

Learn how to work with Natural Language Processing with Python using traditional Machine  Learning methods. Then, deep dive into the realm of Sequential Models and state of the art  language models. 

SELF-PACED MODULE: DEMYSTIFYING CHATGPT AND APPLICATIONS 

Gain an understanding of what ChatGPT is and how it works, as well as delve into the  implications of ChatGPT for work, business, and education. Additionally, learn about prompt  engineering and how it can be used to fine-tune outputs for specific use cases. 

SELF-PACED MODULE: CHATGPT - THE DEVELOPMENT STACK  

Dive into the development stack of ChatGPT by learning the mathematical fundamentals that  underlie generative AI. Further, learn about transformer models and how they are used in  generative AI for natural language.

Capstone Project

Get your hands dirty with a real-time project under industry experts’ guidance. This covers  everything from an introduction to Python and Artificial Intelligence to Machine Learning.  Successful completion of the project will earn you a Post Graduate Certificate. 

 

Year 2

Introduction to Data Science

Data is the core asset of organizations in all domains. Managing that data and extracting  actionable results is key to business survival and success. This course introduces learners to  Data Science. It provides an interdisciplinary overview of the various domains integrated into  Data Science including Business Acumen, Quantitative Analysis, Data Storage and Retrieval  Technologies, Visualization, and presentation methodologies. 

 

Network Fundamentals

This course will provide an introduction to networks. Learners will explore critical networking  concepts in an enterprise environment. Networking Design, Security, Implementation, and  Remote Connectivity will be explored through hands-on labs and assessment. 

Operating Systems and Virtualization

This course will provide an introduction to operating systems implementation in an enterprise  setting. Learners will set up and configure client and server operating systems in networked  environments to provide critical network services in an enterprise environment. Critical  Infrastructure, Setup, Maintenance and Troubleshooting concepts will be explored for future  coursework.

Data Virtualization and Predictive Modeling

The goal of this course is to expose learners to visual representation methods and techniques  that increase the understanding of complex data. They will learn how to take raw data, extract  meaningful information, use Statistical Tools, and make Visualizations to improve comprehension,  communication, and decision making.

Mathematics of Artificial Intelligence and Machine Learning

This course presents critical mathematical concepts used in Artificial intelligence and Deep  Learning. It also focuses on Linear Algebra and Analytic Geometry for AI.

Data Storage Technologies

Database storage technologies have transformed into complex systems that support knowledge  management and decision support systems. This course takes a look at the foundations of  database storage technologies. Professionals will learn about Database Storage  Architecture—types of Database Storage Systems (legacy, current and emerging), Physical Data  Storage, Transaction Management, Database Storage APIs, Data Warehousing, Governance and  Big Data Systems. The learner will tie this all together to see how Database Storage Technologies  apply to Data Analytics.

Capstone or Practicum

The Capstone Project provides the opportunity for integrating program learning within a project  framework. Each learner identifies or defines a professionally relevant need to be addressed  that represents an opportunity to assimilate, integrate, or extend learning derived through the  program. The learner will work with the Capstone Project Advisor to develop a proposal. After  review and approval by the Capstone Project Advisor, they are authorized to complete the  project. Learners present the completed project at a Capstone Fair at the end of the semester. The Practicum course enables students to enhance their knowledge in Artificial Intelligence and  Machine Learning through practical hands-on experience at a business.