Curriculum
The curriculum is designed by leading experts at the University of Arizona and practicing industry professionals. It is specifically curated to cover a range of information science skills - starting from the fundamentals and progressing to more complex, hands-on applications. The program ensures that learners without prior expertise in data science and machine learning too can gain the latest knowledge thereby making it one of the best Information Science programs.
- Curriculum designed by The University of Arizona Faculty and industry Experts
- 11 Projects including a Capstone Project to prepare you for real-world problems
Curriculum Snapshot
Foundations of Information
- Sensing the Data
Learn about types, sources and management of internal and external data including static vs. streaming data, reports, databases and online data.
- Data Collection
Learn the process of extracting, transforming and making data available for further use.
- Data Usability
Learn the fundamentals of scoping the data by eliminating redundant elements and following logical, graphical, statistical analysis flow.
- Data Storage
Get introduced to RDBMS, SQL, NoSQL, Data Marts, Data Lakes, ETL and Data Pipelines as well as large information repositories and tools like Hadoop, Hive, Spark.
Data Mining and Discovery
- Introduction to Data Mining
Learn about data distributions and hypothesis testing. Use basic maths and common programming functions to handle data.
- Business Problem Identification and Scoping
Map data to business problems being solved and select appropriate elements of data.
- Graphical Data Analysis
Learn how to create basic graphs and analyze data visually. Use descriptive statistics to interpret data characteristics.
- Unsupervised Learning Techniques
Use unsupervised learning like dimensionality reduction and clustering to discover elements of data.
Data Analysis and Visualization
- Theory of Visualization
Learn about design, shapes and color theory behind visualizations.
- Single and Multiple Dimension Visualizations
Use appropriate graphical design to depict information and integrate multiple elements of data to present compact and effective information.
- Visualization for Audience
Learn about selection and presentation of visual information according to audience requirements.
- Interactive Visualizations
Understand linking to databases, filtering and information highlighting for visualizations. Explore Python integrations with D3.js
Introduction to Machine Learning
- Linear Modelling
Learn how to do linear regression and linear classification with perceptrons. Extend the linear model to various use cases.
- Learning Theory and Model Evaluation
Understand bias-variance trade off and cross validation.
- Probabilistic Methods
Learn about maximum likelihood and bayesian approach including Priors, Marginal Likelihood and Hyperparameters.
- Optimization and Approximation Methods
Use Gradient Methods (gradient descent, Newton’s method), sampling and Markov Chain Monte Carlo Simulations (using metropolis hastings algorithm).
- Classification Techniques
Implement basic classification techniques like Logistic Regression, Bayesian Classification, Naive Bayes, Nearest Neighbors, Support Vector Machines.
- Domain Specific Techniques
Understand Nonparametric Bayesian Methods, Gaussian Processes, Topic Modelling, Ensembles, Boosting and Random Forests.
Data Warehousing and Analytics in the Cloud
- Introduction to Cloud
Learn Cloud Computing basics for Machine Learning and Data Science. Explore Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Function as a Service (FaaS) in the context of MS Azure.
- Setting up Cloud and Parallel Processing
Learn about Cloud storage systems and virtualization. Use parallel programming tools and infrastructure in the cloud like HDFS, Hive, SparkML.
- Data Warehousing
Develop data warehouses in the cloud. Develop and deploy Machine Learning models in the cloud.
- Data Warehouse Design
Use SQL and NoSQL Data Warehouse to understand designing and querying Data Warehouses.
Text Retrieval and Web Search
- Introduction to Text Retrieval
Learn about boolean retrieval, term vocabulary, posting lists dictionaries and tolerant retrieval.
- Vector Space Modelling
Understand index construction, index compression, scoring, term weighting and development of the vector space model.
- Information Retrieval
Learn about evaluation in information retrieval, probabilistic information retrieval and language models for information retrieval.
- Text Classification and Clustering
Learn about techniques like text classification and Naive Bayes, Vector Space Classification along with Flat Clustering and Hierarchical Clustering.
- Text Handling Techniques
Learn about Expectation Maximization Algorithm, Latent Semantics Indexing, Probabilistic Latent Semantic Analysis and Latent Dirichlet allocation.
- Web Search Basics
Learn about web crawling and indexes and understand link analysis on the internet.
Neural Networks
- Introduction to Neural Networks
Learn about history, development and applications of Neural Networks.
- Feed Forward Neural Networks
Learn about Neural Network architecture, gradient based learning and back propagation.
- Regularization
Understand dropout, parameter penalties and early stopping.
- CNN and RNN
Understand Convolution and Pooling. Apply gated recurrent network along with encoder-decoder network and understand transformer network.
- Practical Considerations and Interpretability
Understand default baseline models and debugging and use local surrogates and saliency maps for interpretability.
Applied Natural Language Processing
- Distribution Similarity
Understand basic count-based methods and word embeddings.
- Sequence Models
Learn about HMM, MEMM, LSTM, and applications to part-of-speech tagging and information extraction.
- Structured Learning
Implement cases of shift-reduce algorithms, PCFG, tree LSTM, and applications to syntactic parsing.
- Alignment Models
Understand alignment models and applications to machine translation and question answering.
- Advanced Techniques
Learn about reading comprehension, question answering and summarization.
Artificial Intelligence and Machine Learning
- Computer Vision Application of Machine Learning
Understand image formats, image manipulation, Machine Learning and Neural Networks to classify images.
- CNN Architectures and Transfer Learning
Implement latest CNN architectures with transfer learning as well as object classification use cases by deploying latest techniques including VGG16, ResNet60.
- Object Detection
Understand object detection approaches like brute force, sliding window, regional proposal and implement latest algorithms like faster RCNN, SSD, YOLO.
- Generative Modelling
Use Generative Adversarial Networks (GANs) for industry-related applications like style transfer and text-to-image conversion.
Capstone Project
*Capstone projects should be aligned with the research priorities of the university. Some examples include:-
- Predicting climate change patterns using hydrological data from regions.
- Predicting climatic vulnerability based on historical data.
- Using NLP in analysis of public health records and predicting population health parameters.
- Using non-invasive imaging techniques to rapidly assess population health.
*Actual capstone projects will be assigned by the program faculty.
Master's Degree from The University of Arizona
Upon the successful completion of Information Science with Specialization in Machine Learning program, you would receive a Master’s Degree from The University of Arizona.
Languages and Tools covered
and more...
Meet the Faculty
Learn from the esteemed faculty at University of Arizona and practicing Industry Professionals with immense experience in the field of Information Science.
Dr.Steven Bethard
Associate Professor, School of Information
Dr. Christian Roman-Palacios
Assistant Professor of Practice, School of Information
Dr. Hong Cui
Professor, School of Information
Dr. Bryan Heidorn
Professor, School of Information
Director, Center for Digital Society and Data Studies.
Dr. Meaghan Wetherell
Assistant Professor of Practice, School of Information
Dr. Peter Jansen
Assistant Professor, School of Information
Get The University of Arizona Advantage
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Handshake
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On-Campus Experience
Interact with top-notch faculty and global peers at the University of Arizona campus in Tucson, Arizona.
Great Learning Advantage
Learn from a comprehensive curriculum taught by world-class faculty. Get guidance on your learning journey, and access dedicated career support.
DESIGNED FOR PROFESSIONALS/FRESHERS
Learn while pursuing your career
- Hybrid format with personalised mentorship
- Practical Insights from Industry Experts
STRUCTURED PROGRAM WITH GUIDANCE
Networking and Program support
- Dedicated program manager to solve your queries
- Mentorship from experts to gain industry insights
- Interact with peers to grow your professional network
Learner Testimonials*
David Phillips
Master of Science in Information Science
The program is delightful, and the professors taught me so much. I have absolutely enjoyed the program.
Prashanth Shenoy
L&TD Professional at a Fortune 500 Fintech Company
My favourite part of the MSML program are industry expert sessions. Great Learning absolutely went that extra mile to bring in some of the best experts to demonstrate the concepts taught by the UoA professors.
Loren Champlin
PhD student in Information Science
I found the research in AI, game design and ML really insightful. The faculty here finds time to talk to learners and has you best interests at heart.
Mona Baid
Member of Technical Staff
My favourite part of the program has been the hands-on projects which are incredibly engaging and stimulating. I have been able to apply the concepts and skills that we have learned in class to real-world scenarios.
Sarah Stueve
PhD student in Information Science
I was exposed to different projects in a breadth of areas. The University and Program caters to people from different backgrounds.
*Testimonials from learners of School Of Information, University Of Arizona
Fees and Application Details
MS in Information Science: Machine Learning Program
USD 34,176
*Save USD 67,750 as compared to full-time US master’s
Earn Master’s Degree from
- 2 years Program
- Hybrid mode of learning with first year online and second year on-campus in US
- Comprehensive curriculum with hands-on learning and mentorship sessions
- Quick application with no additional tests or prerequisites
- Globally recognized Master’s degree from the University of Arizona
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Application Process
1
Apply Online
Complete filling a fast and easy online application form. No additional tests or prerequisites are needed to apply.
2
Pre-screening
Our team will make contact with you by phone to confirm your eligibility for the program.
3
Application Assessment
The Admissions team will assess your application and provide a timely response.
4
Join The Program
If selected, you will receive an acceptance letter with instructions on how to pay and join the program.
Great Learning provides end-to-end support in applying to the University of Arizona and also in the US visa application process during the first year.
Who is this program for?
Early Career Professionals
- Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods.
- Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods.
Mid & Senior Level Professionals
- Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world.
- Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world.
Working Professionals
- Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge.
- Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge.
Upcoming Application Deadline
Our admissions close once the requisite number of participants enroll for the upcoming
batch . Apply early to secure your
seats.
Deadline: 30th Jun 2023
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Now