Advanced Certificate Programme in Applied Artificial Intelligence and Machine Learning

Program Description

  • Understand and apply fundamental concepts of AI and ML.
  • Apply deep learning techniques using state-of-the-art frameworks.
  • Implement AI solutions in real-world scenarios.
  • Develop and deploy ML models for various applications.

Key Highlights

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On successful completion of the programme, you will

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Be awarded a completion certificate on " Advanced Certificate Programme in Applied Artificial Intelligence and Machine Learning”.

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GitHub profile building

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Getting started with Kaggle

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Research paper discussion

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3 IBM professional certifications in AI and ML

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Optional 2-day immersion to IIT Madras Research Park for successful learners

Learning Format

Online

Duration

11 months Online + Live sessions
Live sessions on Saturday or Sunday

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and
Emeritus

Program Fee

INR 1,50,000 + GST

Downloads

Education Qualification

Graduates (10+2+3) from a recognised university in any discipline

Suggested Prerequisites

Graduate/diploma holders can apply (basic Math and programming knowledge is preferred)

Lead Faculty

Prof. C. Chandra Sekhar
Programme Coordinator
Professor at IIT Madras (2001-Present)

Professor C. Chandra Sekhar is a distinguished faculty member in the Department of Computer
Science and Engineering at IIT Madras. He was the Head of Department of the CSE Department
at IIT Madras from 2019 to 2022. His expertise spans speech recognition, neural networks, kernel
methods, machine learning, deep learning, and metric learning. A highly respected researcher,
Prof. Sekhar has authored numerous papers featured in prestigious national and international
peer-reviewed journals.

Prof. Dileep A. D.
Programme Faculty
HoD of CSE Dept, IIT Dharwad

Dr. Dileep A. D. is the Head of Department of Computer Science and Engineering at IIT Dharwad.
He has 10+ years of teaching experience across institutions like IIT Madras, IIT Mandi, and IIT
Dharwad. He is widely recognised for his expertise in pattern recognition, kernel methods, machine
learning, speech technology, and computer vision. He earned both his M.Tech and PhD in
Computer Science and Engineering from IIT Madras, Chennai. A prolific researcher, Dr. Dileep has
contributed extensively to the field, with numerous publications in prestigious peer-reviewed
journals.

Learning Schedule

  • Overview of topics to be covered in the Programme
  • Motivation for the Programme
  • Overview of the Programme
  • Expected Outcomes of the Programme
  • Networking Session and Project Groups
  • Brief about software/tools

  • Linear algebra: Vectors, matrices, inner products, matrix-vector multiplication, eigen
    values/vectors, singular value decomposition
  • Calculus: Differentiation (single/multiple variables, vectors, and matrices),
    unconstrained and constrained optimisation (Lagrangian multiplier)
  • Probability Theory: Discrete and continuous random variables, probability
    distributions, Bayes' rule, Gaussian density function, conditional probability
  • Statistics: Descriptive and inferential statistics, hypothesis testing, probability
    distributions

  • SQL Basics: RDBMS & NoSQL, MS SQL Server & MongoDB demos, SQL tables,
    joins, subqueries, views, functions, pattern matching, UDFs, stored procedures,
    ranking, and sorting
  • Advanced SQL: Mathematical and date-time functions, SQL ROLLUP, record
    grouping, common table expressions, clustered indexes
  • Data pipeline integration: Integrating ML models into data pipelines

  • Python: Pre-read
  • Python details: Python syntax, factors, NumPy, Scipy, Pandas, Data Visualization,
    Scikit Learn, Pytorch,Matplolib, Seaborn Tensorflow, Deployment and
    productionisation
  • Advanced python techniques: generators, iterators, decorators, context managers,
    performance optimisation techniques. Demo on Python tools, python packages,
    pytorch, scikit learn, tensorflow, demo of deployment on python, demo on advanced
    python techniques

  • EDA: Data types and variables, central tendency and dispersion
  • Five point summary and skewness, Box-plot, covariance and correlation, encoding,
    scaling and normalisation.
  • Focus on pre-processing, missing values, working with outliers, demo on EDA

  • NLP and text processing applications: Text classification, parts-of-speech tagging,
    named entity recognition, text summarization, text question answering, machine
    translation. Demo on sentiment analysis, chatbot creation and text-to-text translation
  • Image and video processing applications: Image classification, image annotation,
    image captioning, video classification, video captioning, visual question answering,
    visual common-sense reasoning
  • Speech processing applications: Speech recognition, speaker recognition, speech
    emotion recognition, spoken language recognition, text-to-speech synthesis,
    speech-to-speech translation

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Active learning
  • Self-supervised learning
  • Transfer learning
  • Domain adaptation, Zero-shot
  • One-shot and Few-shot learning; Federated learning

  • Linear model for regression
  • Supervised learning
  • Parameter estimation
  • Overfitting
  • Regularisation
  • Ridge regression

  • K-nearest neighbour classifier
  • Bayes classifier
  • Normal density function
  • Decision surfaces
  • Naïve Bayes classifier
  • Maximum likelihood estimation
  • Gaussian mixture model

  • Distance of a point to a hyperplane
  • Margin of a separating hyperplane
  • Hard-margin SVM
  • Soft-margin SVM
  • Kernel functions
  • Multi-class classification using SVMs

  • Principal component analysis
  • Fisher discriminant analysis

  • Construction of decision tree for classification
  • Random forest classifier

  • Bagging
  • Boosting
  • AdaBoost
  • Applications of Ensemble methods

  • K-Means clustering
  • Hierarchical clustering
  • Applications of Clustering Techniques

  • McCulloch-Pitts neuron
  • Perceptron learning rule
  • Sigmoidal activation function
  • ReLU activation function
  • Softmax activation function
  • Multilayer feedforward neural network
  • Error backpropagation method
  • Gradient descent method
  • Stochastic gradient descent method
  • Stopping criteria, Logistic regression based classifier
  • Focus on Deep Learning using Tensorflow and Keras, understanding Feedforward
    neural network, back propagation, gradient descent and logistic regression

  • Generalized delta rule
  • AdaM based optimizer
  • Regularization: Drop-out, Drop-connect, Batch normalization

  • Basic CNN architecture, Rectilinear Unit (ReLU), 2-D Deep CNNs: LeNet, VGGNet,
    GoogLeNet, ResNet
  • Image classification using 2-D CNNs
  • 3-D CNN for video classification
  • 1-D CNN for text and audio processing
  • Object localization and detection algorithms – YOLO, Image Segmentation, and
    UNet

  • Architecture of an RNN, Unfolding an RNN, Backpropagation through time
  • Long short-term memory (LSTM) units
  • Gated recurrent units
  • Bidirectional RNNs
  • Deep RNNs

  • Structure of GAN, types of GAN models, applications of GAN models

  • Attention mechanism
  • Transformer architecture
  • BERT (Bidirectional Encoder Representations from Transformers)
  • ViLBERT
  • GPT (Generative Pre-trained Transformer)
  • Applications of transformer models

  • Applications of Gen AI in different domains
  • Examples of prompt engineering, fine tuning and API creation and integration

  • Markov Decision Processes (MDPs)
  • Q-Learning and Deep Q Networks (DQN)
  • Actor-Critic models
  • Exploration vs. Exploitation strategies

  • Ethical considerations (banking, ecommerce sectors); pushing code to repository
  • Responsible AI
  • Explainable AI
  • Registry, Model & Data Monitoring

  • Understanding cloud infrastructure essentials
  • Cloud-based ML Services and Databases
  • Containerization
  • Cloud enablement - scalability and flexibility
  • Understanding emerging themes: FaaS, Edge Computing
  • Federated Learning
  • AutoML
  • Explainable AI
  • Cloud ML-Ops
  • Deployment on Gemma models on Vertex AI and Kubernetes engine Scaling using AWS

  • The capstone project is a comprehensive, real-world assignment in which
    participants apply their knowledge and skills to solve industry-specific problems
  • It integrates concepts from their coursework, encouraging critical thinking and
    innovation
  • Capstone projects help participants gain hands-on experience, making them
    industry-ready by demonstrating their ability to tackle complex challenges in a
    professional setting


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