Foundations of Modern
Machine Learning

About Course


iHub-Data(IIIT-Hyderabad) is dedicated to enhancing the quality of education in cutting edge areas of Artificial intelligence. It has strong research programs in machine learning, image processing, computer vision, robotics, natural language processing, pattern recognition and speech processing. iHub-Data is happy to announce a foundation program in machine learning for aspiring engineering students across India.

"Registration process for FMML 2021 is over. The FMML-2021 program has commenced on 03rd Oct 2021 (view launch event here). Dates for the next FMML program will be announced soon."

Who can participate?

  • Students from various engineering colleges across India
  • Undergraduate engineering students ( third and fourth year) from the following streams:
    1. Computer Science/Information Technology or other allied branches
    2. Electronics Engineering or other allied branches
    3. Electrical Engineering or other allied branches

What makes this program unique?

  • 50-week certificate program in Modern Machine Learning (equivalent to a typical 4 credit course as per AICTE/UGC norms)
  • Online modules with personalised learning experience
  • Equal focus on foundation and practices
  • Cash awards on successful completion
  • Discussions with eminent researchers from academics and industry

What is the qualifying criteria?

  • Students need to be nominated by their respective college through their HoD’s
  • Selected students undergo a preparatory module
  • The final selection of students is based on student performance during the preparatory module

Course Fees

The participation fee for this 50-week course would be INR 10,000 + tax. Financial assistance will be provided for deserving students.



team member
C. V. Jawahar
IIIT Hyderabad
team member
Anoop M. Namboodiri
IIIT Hyderabad
team member
Ravi Kiran Sarvadevabhatla
IIIT Hyderabad
team member
C.K. Raju

Teaching Team


team member
Thrupthi Ann John
I am a research scholar working with Prof. C V Jawahar and Prof. Vineeth N Balasubramanian at the Computer Vision and Information Technology Lab (CVIT) in IIIT Hyderabad. My research interests include deep algorithms for face tasks, visualization of deep algorithms and solving computer vision problems using deep learning. In my spare time, I like to draw and paint.
team member
Ekta Gavas
I am a graduate research student at IIIT Hyderabad working with Dr. Anoop Namboodiri. My areas of interest are Deep Learning and Computer Vision. I have worked on a few small-scale projects in these areas here at IIIT. Currently, I am exploring the field of biometrics with Deep Learning, in particular, face recognition for my research.
team member
Pranav Tadimeti
I am a research student at the Centre for Visual Information Technology, IIIT-H. My work involves experimenting with various Computer Vision detection techniques to extract various regions of interest in ancient Indic manuscripts.
team member
Animesh Sinha
Hi! I am a research student at IIIT. I am most interested in Algorithmic Theory (competitive programming 😋), and Intelligent Systems. For my research, I work on Reinforcement Learning Algorithms applied to Compilation and Algorithms for Near-term Noisy Quantum devices. More details are at
team member
Bhuvanesh Sridharan
Hello! I am a research student at the Centre for Computational Natural Science and Bioinformatics, IIIT Hyderabad. My work primarily involves using Deep Reinforcement Learning and ML to solve tasks pertaining to inverse molecular problems and Chemistry.
team member
Tanish Lad
Hello! I am a research student at the Language Technologies Research Centre, IIIT Hyderabad. My work primarily involves using Deep Learning and ML to solve tasks pertaining to Text Classification in Indian Languages.
team member
Vinod Kumar Kurmi
I am a post-doc research fellow at KU Leuven, Belgium. Earlier, I was a post-doc fellow at IIIT Hyderabad. I obtained Ph.D. and M.Tech from Indian Institute of Technology Kanpur in 2020 and 2014, respectively. My research areas are related to Computer Vision (CV), Deep Learning (DL), and Machine Learning (ML). I work on problems related to domain adaptation, incremental learning, and multimodal representation learning. I am also interested in working in fairness in ML, NLP, and speech processing problems.
team member
Sahil Manoj Bhatt
I am a research student at the Language Technologies Research Centre, IIIT Hyderabad. My work primarily involves exploring various Deep learning architectures and applying them to solve various tasks in the areas of Natural Language Processing, such as Summarization and Text Classification. Other areas of interest include Computer Vision and Data Analytics.
team member
Arpan Dasgupta
I am a research student at the Center for Security Theory and Algorithms at IIIT Hyderabad. I work on large scale machine learning optimizations. My interests lie in mathematics, ML and computer programming.
team member
Pranav Kirsur
Hello! I am currently a research student at the Center for Visual Information Technology, IIIT Hyderabad. I am working on problems related to 3D Reconstruction, Neural Rendering, and using Event Cameras for vision applications.
team member
Kushagra Agarwal
Hi! I am a research student at the Centre for Computational Natural Science and Bioinformatics, IIIT Hyderabad. My research work primarily involves understanding SARS-CoV-2 dynamics through viral RNA sequence analysis and Minimizer sampling-based Algorithms for Genome Size Estimation. I have also been working on the application of ML in Parkinson's Disease Prediction, MOSFET optimization and Bone segmentation using Micro-CT scans of mice.
team member
Yoogottam Khandelwal
Hello! I am currently a researcher at the Centre for Visual Information Technology (CVIT), IIIT Hyderabad. I'm really interested in Computer Vision and Deep Learning. I'm currently working on problems related to speech to lip generation.

Invited Speakers



This 50-week course will discuss in detail, important contemporary topics in machine learning through video lectures, laboratory experiments and projects. The broad contours of modern machine learning will include the following topics:

  • Representation and Learning
    • Feature Vectors, Feature Spaces
    • Feature Extraction
    • Learning Problem Formulation
  • Appreciating and Interpreting Data
    • Dimensionality Reduction
    • Data Visualization
  • Classification
    • Nearest Neighbour methods
    • Linear Classifiers, Perceptrons, Gradient Descent
    • Multi-class classifiers
    • Decision Trees
  • Experimentation Methods
    • Training, Testing and Validation
    • Overfitting and Generalization
    • Feature Engineering
    • Performance Metrics
  • Probabilistic Methods
    • Bayes and Naive Bayes Classifiers
    • Mixture Models
    • MLE and MAP Estimates
  • Unsupervised Learning and Clustering
    • K-Means, EM and Mixture Model Fitting
    • Similarity Metrics, Criterion Functions
    • Graph-based clustering; Hierarchical Clustering
  • Regression
    • Linear and Logistic Regression
    • Regularization
  • Neural Networks
    • Multilayer Perceptrons
    • Back-propagation, Training strategies
  • Deep Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Autoencoders
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We will update soon.

Cash Awards

As a one-time incentive, all participants of the first batch who have paid fees in full and who successfully complete the program, would be extended cash awards.

Here are some Frequently Asked Questions

Contact us

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