1st class free
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Aadirupa
- Rate ₹3,777
- Response 2h
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Students1
Number of students accompanied by Aadirupa since their arrival at Superprof
Number of students accompanied by Aadirupa since their arrival at Superprof
₹3,777/hr
Contact
1st class free
- Basic computer
- Machine Learning
Learn Machine Learning from Scratch — From a former Apple ML Researcher & UIC CS-ML Professor
- Basic computer
- Machine Learning
Class location
About Aadirupa
Aadirupa Saha has been an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC) since Fall 2025. She is a member of the UIC CS Theory group, as well as IDEAL Institute. Prior to this, she was a Research Scientist at Apple MLR, working on Machine Learning theory. She completed her postdoctoral research at Microsoft Research (NYC) and earned her PhD from the Indian Institute of Science (IISc), Bangalore.
Saha's primary research focuses on AI alignment through Reinforcement Learning with Human Feedback (RLHF), with applications in language models, assistive robotics, autonomous systems, and personalized AI. At a high level, her work aims to develop robust and scalable AI models for designing prediction systems under uncertain and partial feedback.
[Optional] Specifically, Saha is deeply motivated by the tremendous potential of AI to democratize learning—reshaping our current education system into a truly adaptive, accessible, and personalized experience for every learner! Driven by this transformative power of generative AI and language models, she envisions building the foundations for equitable, intelligent education systems that turn this vision into reality. Her research focuses on developing futuristic educational models by leveraging her expertise in AI alignment with human feedback, alongside tools from Machine Learning (Online Learning, Bandits, and RL theory), Optimization, Federated Learning, Differential Privacy, and Mechanism Design.
[Optional] Saha has been a part of several organizational efforts and tutorials over the last few years. Notably, she serves as the communication chair for RLC, 2026. Besides her community services include a keynote talk at DA2PL Conference, [NeurIPS, 2023] tutorial on Preference Learning, [UAI, 2023] tutorial on Federated Optimization, two tutorials at [ECML, 2022], [ACML, 2021], three ICML workshops [ICML, 2024], [ICML, 2023], [ICML, 2022], an IDEAL special program workshop, and two TTIC workshops [TTIC, 2023], [TTIC, 2022]. In addition, Saha has also served in several panel discussions and senior reviewing committees for major ML conferences.
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About the class
- Classes 1 to 5
- Classes 6 to 8
- Class 10
- +14
levels :
Classes 1 to 5
Classes 6 to 8
Class 10
Classes 11 & 12
Class 12
BTech
Class 11
Adult Literacy
Masters
PhD
MBA
Kindergarten (LKG, UKG)
Beginner
Intermediate
Advanced
Professional
Children
- English
All languages in which the class is available :
English
I am Aadirupa Saha, an Assistant Professor of Computer Science at the University of Illinois Chicago (UIC), formerly a Research Scientist at Apple Machine Learning Research and Postdoc at Microsoft Research New York. My teaching is built on one belief: anyone can learn Machine Learning if it is taught with the right intuition, not just code and formulas.
My approach starts with building strong foundations before touching any tool or library. I teach the why behind every algorithm — why gradient descent works, why we need regularization, why neural networks can approximate any function. I use visual explanations, real datasets, and hands-on coding exercises in Python to make every concept concrete and memorable. I ask guiding questions rather than giving away answers, so you build genuine problem-solving instincts that last beyond our sessions.
A typical lesson begins with a 5-minute diagnostic to find exactly where your understanding needs work. We then spend 20 minutes rebuilding that concept cleanly from first principles. The next 20 minutes are guided practice where we code or solve problems together. The final 10 minutes are yours — you work independently while I observe and give precise feedback. Every session ends with a clear action plan for what to practice next.
What sets me apart is simple. I am not a grad student or a bootcamp instructor. I am an active AI researcher who has published at NeurIPS, ICML, and UAI, delivered tutorials at top ML conferences, and built ML systems at Apple and Microsoft. I bring both deep theoretical understanding and real industry experience into every lesson.
These lessons are designed for undergraduate and graduate students taking their first ML or AI course, software engineers and data professionals transitioning into ML roles, researchers from other fields wanting to apply ML in their work, and ambitious self-learners who want a rigorous structured path into AI.
LONG TERM COURSE OUTLINE
Module 1 — Math Foundations: Linear algebra, probability, statistics, and calculus for ML. No prior advanced math required.
Module 2 — Python for ML: NumPy, Pandas, Matplotlib, and Scikit-learn. Hands-on from day one.
Module 3 — Core ML Algorithms: Linear regression, logistic regression, decision trees, SVMs, k-means clustering, and model evaluation.
Module 4 — Deep Learning Fundamentals: Neural networks, backpropagation, CNNs, RNNs, and intro to PyTorch.
Module 5 — Advanced ML Topics: Reinforcement Learning, Bandits, Online Learning, and introduction to RLHF and AI alignment.
Module 6 — Real World ML: Building end-to-end ML pipelines, working with real datasets, model deployment basics, and career guidance in ML and AI.
Each module includes reading materials, coding assignments, and a mini project. Students completing the full course will have a strong portfolio and the foundations to pursue ML research or industry roles confidently.
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Rates
Rate
- ₹3,777
Pack prices
- 5h: ₹15
- 10h: ₹30
online
- ₹3,777/hr
free classes
This first free class with Aadirupa will allow you to get to know each other and to specify the exact learning requirements for the upcoming classes.
- 1hr
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