Take the next step towards mastering AI & ML

PG Program - Artificial Intelligence & Machine Learning (Advanced)

Online | 3.5 Months
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Applications Close Tomorrow
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World Rank #4

Business Analytics, QS Business, Analytics Ranking (2020)

Who is this program for?

The PGP-AIML (Advanced) is a follow-up program to the PGP-AIML, designed to cover advanced concepts in the domains of Machine Learning, NLP, Neural Networks, and Computer Vision. It is suited for:

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A current participant of the PGP-AIML program.

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Professionals who are familiar with the basics of ML, NLP, Computer Vision & Neural Networks.

Why Join PGP-Artificial Intelligence and Machine Learning (Advanced)?

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Certificate from UT Austin

Ranked #4 worldwide in Business Analytics

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Unique Mentored Learning

Live and interactive learning in small groups

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Learn Advanced Concepts

Learn advanced concepts of ML, NLP, Computer Vision & Neural Networks

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All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University.

KUMAR MUTHURAMAN

KUMAR MUTHURAMAN

Faculty Director, PGP-AIML

H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics

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MS & PhD: Stanford University

Program Structure

A structured 3.5-month online program with hands-on projects and mentor-led sessions on weekends

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Recorded Online Lectures

Access best-in-class recorded content from UT Austin faculty, global academicians and accomplished industry experts.

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Hands-on Projects

Apply your skills in real-time as you work end-to-end on 4 hands-on projects, focused on real-world business problems.

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Weekend Mentor-led Sessions

Practice on data sets and gain practical insights from leading data science & analytics professionals in live sessions.

Unique Mentored Learning

Personalized Attention:
Mentoring in Small Groups

Self-learning online can be difficult.
Our mentored learning model brings you 2 hours of live and interactive mentor-led sessions every weekend in groups of 10 learners (micro-class).

Mentors are:

  • Professionals working in leading global companies.
  • Industry experts with several years of domain experience.
  • Matched to your domain and experience level.
  • A valuable means of networking and gaining career insights.
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Mentoring is interactive and happens in small groups

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Our Mentors work at the best companies

Mentors working Companies

PGP-Artificial Intelligence and Machine Learning (Advanced) Experience

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Personalized Learning

Each module has recorded content that is followed by a session with one of our distinguished industry mentors, in a small group of participants. These mentors are thought leaders in different domains with several years of industry experience that enables them to impart practical knowledge and real-world insights.

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Webinars with UT Austin Faculty

Participate in monthly webinars with leading UT Austin faculty to learn the applications of AI & ML to different industries through case studies and live examples.

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Experiential Learning Projects

Interspersed throughout the program, the 4 hands-on projects help you learn the real-world applications of concepts by applying them to real-world business problems.

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Career Enhancement Sessions

Prepare for a successful career transition through career guidance by industry practitioners, focusing on resume and LinkedIn profile review, interview preparation and 1:1 career coaching.

ePortfolio

As you complete your experiential projects, we will automatically create a document to showcase your learning & projects in a snapshot that we call an ePortfolio. This can also be easily shared on social media channels to establish your credibility in Artificial Intelligence & Machine Learning with potential employers.

E-Portfolio

Curriculum

The PGP-AIML (Advanced) curriculum has been carefully designed by UT Austin faculty and leading industry experts to help you master advanced AI and Machine Learning concepts in 4 key areas.

Machine Learning - Advanced
  • Advanced EDA - Missing Value & Outliers Treatment and Dimensionality Reduction (PCA & LDA)
  • Clustering - DBScan, HDBScan
  • Supervised Learning and Model Tuning - Regularization, SVM & Boosting Techniques, SMOTE & ADASYN
  • Project 1
Neural Networks - Advanced
  • Auto Encoders & Transfer Learning
  • Hyperparameter Optimization: Tuning the Learning Rate
  • Project 2
Computer Vision - Advanced
  • Architectures - AlexNet, GoogleNet, ResNet, ExcitationNet, TSNE, Pooling & Visualizations
  • Semantic Segmentation & Object Detection
  • Face Recognition (Siamese Network as Metric Learning)
  • Project 3
Natural Language Processing - Advanced
  • Advanced RNN and LSTM Architecture
  • LSTM Application
  • Project 4

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Learn from the Best

Learn from leading academicians in the field of Artificial Intelligence and Machine Learning and several experienced industry practitioners from top organizations. An indicative list of Artificial Intelligence and Machine Learning experts engaged with us include:

Hands-On Learning from Artificial Intelligence and Machine Learning Practitioners

Get invaluable input from the who's who of the industry:

PGP-Artificial Intelligence and Machine Learning alumni work for world-class companies

Alumni working companies

Admission Details

Eligibility

  • done An undergraduate/bachelor’s degree, with a minimum of 50% aggregate marks or equivalent.
  • done Thorough grounding in the basics of Machine Learning, NLP, Computer Vision & Neural Networks.
  • done Prior programming experience in Python.
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Selection Process

Step 1

Application Form

Register by filling up the online application form >

Step 2

Call from your Program Advisor

A program advisor is assigned to assist you through the selection process and answer all your questions.

Step 3

Shortlisting and Panel Review

A panel will review your application to determine your fit for the program. The panel will evaluate you on your work experience, past academic credentials and motivation for joining the program.

Step 4

Interview/Screening

If shortlisted, you will go through a telephonic interview (this may be waived for candidates with strong profiles and experience).

Step 5

Admissions Offer

After a final admissions committee and faculty review, if selected, you will receive an ‘offer of admission’ letter for a seat in the upcoming cohort of the program.

Fee Details

Pay the full fee in easy installments. For more information, please reach out to a Program Advisor.

3,500 USD

Pay upfront and get a discount of 175 USD

3,325 USD

Payments

Candidates can pay the program fee through

credit_card
Credit/Debit Cards
account_balance
Bank Transfer

Fee Includes

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Tuition Fee

Great Learning material

Learning Material

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Mentorship Sessions

Upcoming Application Deadline

Tomorrow

We follow a rolling admission process and admissions are closed once the requisite number of participants enroll for the upcoming cohort. So, we encourage you to apply early to secure your seat.

Apply Now

Cohort starts on To be announced

Frequently Asked Questions

What is the eligibility for the program?
The Post Graduate Program in Artificial Intelligence and Machine Learning (Advanced) is a hands-on program designed to teach advanced AI & ML concepts. To be eligible, you should be:
  • A current participant of the PGP-AIML program; or
  • Have a thorough grounding in the basics of Machine Learning, Natural Language Processing (NLP), Computer Vision and Neural Networks .
  • Have prior programming experience in Python.
What will my learning schedule be like?
You can learn concepts online with recorded sessions, and clarify your doubts at the end of the week with online mentoring sessions with an industry expert.
What kind of learning assistance will I get?
You will get weekly assistance from expert industry practitioners, who will clarify your doubts, and offer guidance regarding your projects.
Who are the mentors as part of this program?
You can find the details of the mentors in the program page. Suffice to say, the mentors are industry practitioners with leading organizations and come with extensive experience in their fields.
Will I have to spend more on other material?
All required content are included in the program fee and available at any time during (and after completion of) the program. You are welcome to purchase additional material for your own reference on faculty recommendation.
Will the content be available after the program is completed?
We believe that learning is continuous and hence all learning material – lecture notes, online content and supporting material – will be available through the online platform for 3 years after completion of the program.
How will I be evaluated during the program?
In this holistic and rigorous program, you will be evaluated continuously. All quizzes, assignments, attendance and projects are used to evaluate and monitor your progress towards the desired learning outcomes.
Can my company sponsor me?
We accept corporate sponsorships and can assist you with the process. For more information, you can write to us at aiml.utaustin@mygreatlearning.com
What kind of career support can I expect from this program?
The PGP-Artificial Intelligence and Machine Learning (Advanced) offers career support to ensure you derive not just positive learning outcomes, but also the career outcomes you desire for your professional journey. You can expect career guidance through 1:1 career coaching sessions with industry practitioners, resume and LinkedIn profile review, interview preparation sessions, and webinars with UT Austin faculty.
What is the admission process?
You are invited to apply using our online application form. Our admissions panel evaluates all applications and if shortlisted, you will be required to attend an admissions screening Interview.
How can I apply for this program?
If you are interested in the Program, you can apply through the online application form. If you need assistance, please write to us at aiml.utaustin@mygreatlearning.com
Do candidates need to bring their own laptops?
The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment.