Advanced Certificate Programme in AI, ML and DL ( formerly known as Certificate Programme in Machine Learning and Deep Learning ) ( Batch 8)

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• Numerical Computations and Linear Algebra using NumPy

• Data Pre-processing using Pandas

• Data Visualisation using Matplotlib

• Introduction to Scikit-learn

Learning Outcomes

Learn about file handling with Python, plotting and visualisation with Matplotlib, arrays, and matrices with NumPy, scientific computing with NumPy and, data handling with pandas.

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• Linear Algebra: Vectors, Matrices, Norms, Subspaces, Projections, SVD, EVD, Derivatives of Matrices, Vector Derivative Identities, Least Squares

• Optimisation: Constrained and Unconstrained Optimisation, Maxima and Minima, Convex and Non-Convex, Gradient and Hessian, Positive Definite and Semi-Definite, Second Derivative Test, Steepest Descent, Adam, AdaGrad, RMSProp, and KKT

• Probability Theory: Discrete and Continuous Random Variables, Conditional Probability, Joint Probability Distribution, Multivariate, MAP Criterion, and ML Criterion 

Learning Outcomes 

• Gaining an understanding of the mathematical fundamentals crucial for machine and deep learning success, like linear algebra, probability theory, and optimisation methods. In linear algebra, one masters essential operations involving vectors and matrices and the understanding of eigenvalues and eigenvectors. Probability theory will provide concepts on probability distributions and Bayes' theorem, which is crucial to understanding the probabilistic nature of machine learning algorithms. Furthermore, it delves into optimisation techniques, including gradient descent and convex optimisation, empowering to optimise models and algorithms effectively.

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• Differences Between Artificial Intelligence, Machine Learning, and Deep Learning

• Differences Between Statistical Approach, Shallow Learning, and Deep Learning

• Data Types and their properties

• Attribute Types

• General characteristics of datasets

• Data Measurement Criteria: Precision, Bias, and Accuracy

• Data Pre-processing Techniques

• Distance-based Dissimilarities between Datasets

• Machine Learning Problems: Classification, Regression, Interpolation, and
  Density Estimation

• Linear Regression Model, Classification Model, and Classification
  Evaluation

• Learning Algorithms: Supervised and Unsupervised

Learning Outcomes

• Understand and differentiate between key concepts like AI, Machine Learning, and Deep Learning. Gain a strong foundation in data properties, types, and characteristics of datasets. Furthermore, you will be able to evaluate data quality using metrics like precision, bias, and accuracy, and explore pre-processing techniques for data preparation.

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• Bayesian Decision Theory: Bayesian Classifier, Discriminant Functions, Minimum Error Rate Classification

• Naïve Bayes Classifier

• Logistic Regression Model and Parameter Estimation (Maximum-Likelihood)

• Dimensionality Reduction Technique: Principal Component Analysis (PCA)

• Non-parametric Techniques: K-Nearest Neighbour (KNN), Density Estimation

• K-means Clustering

• Decision Tree (Entropy, Gini Impurity Index)

• Demonstration of All Machine Learning Algorithms

Learning Outcomes

• Gain proficiency in data analysis and visualisation techniques essential for extracting insights from datasets. Dive into various machine learning algorithms, including supervised, unsupervised, and reinforcement learning and tasks such as classification and regression. Understand the theoretical background of supervised methods like Linear and Logistic regression, SVM, decision trees and unsupervised methods, including clustering, KNN, and dimensionality reduction techniques (PCA). 

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• Neurons, Perceptron Convergence Theorem, Relation Between the Perceptron and Bayes' Classifier, Batch Perceptron Algorithm, Adaptive Filtering Algorithm, Least Mean Square (LMS) Algorithm, Multilayer Perceptron, Feedforward Operation, Batch and On-line Learning, Activation Function, Backpropagation Algorithm, Rate of Learning, Stopping Criteria, XOR Problem, Loss Function, Bias and Variance, Regularization, Cross-Validation, Early-Stopping Criteria, VM, Radial Basis Function, Bagging and Boosting

• Support Vector Machine (SVM)

• Random Forest, Ensemble Learning, Bagging, and Boosting

• Python Demo on contruction and training of neural networks for classification and regression applications

Learning Outcomes

• Delve into the theory and design of Artificial Neural Networks (ANNs) for classification and regression tasks, mastering essential concepts like backpropagation and stochastic gradient descent for training ANNs. Gain the necessary practical skills to implement all the algorithms using Python libraries like NumPy, pandas, scikit-learn, and Keras.

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

Basics of Deep Learning

• Importance of deep learning

• Learning from large datasets

• Types of data and architectures

• End-to-end model design for feature learning and decision-making

Convolutional Neural Network (CNN)

• Architecture design

• Training methodology of CNN

• Use cases

• State-of-the-art CNN models

• Python demo on object detection/image classification

Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM)

• Modeling of time-series data

• Architecture design of RNN

• Training methodology of RNN

• Architectures of LSTM and advantages over RNN

• Use cases

• Python demo on machine translation, stock prediction

Autoencoder (AE)

• Unsupervised learning

• Architecture design of AE

• Convolutional AE

• Training with unlabeled data

• Use cases

• Python demo in denoising, dimensionality reduction

Generative Modelling

Subtopic 1 - Variational Autoencoder (VAE):

• Fundamentals of generative modeling

• Architecture of VAE

• Estimating data distribution

• Training methodology of VAE

• Use cases

• Python demo for image generation

Subtopic 2 - Generative Adversarial Network (GAN)

• Generative modeling as a game-theoretic approach

• Architecture design of GAN

• Training methodology of GAN

• Use cases Python demo on image generation, style transfer

Subtopic 3 - Diffusion:

• Generative modeling through denoising

• Architecture design of diffusion models

• Training of diffusion models

• Python demo on high-quality image generation

Attention and Transformer

• Attention mechanism

• Advantages of Attention

• Architecture design of Transformers

• Training of Transformer

• Python demo on language translation using Transformer

Transfer Learning

• Leverage knowledge from one task to improve performance on another task

• Pre-training on large datasets

• Fine-tuning DL models on small dataset

• Use cases

• Python demo on transfer learning in computer vision

Knowledge Distillation

• Optimisation of DL models

• Transfer knowledge from a complex teacher model to a simpler student model

• Training methodology for distillation

• Use cases

• Python demo on knowledge distillation in computer vision and natural language processing 

Learning Outcomes

• Understanding the advantages of deep learning. Gain in-depth knowledge of deep architectures such as CNNs, RNNs, LSTMs, GRUs, Attention mechanisms, Transformers, and Autoencoders. A theoretical and practical understanding of the architectures, along with insights into design choices for better model development. Essential model training concepts like regularisation, dropout, data augmentation, batch normalisation, and hyperparameter tuning are explored for effective optimisation. Popular generative methods for AI applications such as VAEs, GANs, and Diffusion models are discussed alongside advanced topics like transfer learning, knowledge distillation, network pruning, and quantisation. Hands-on demos using TensorFlow and PyTorch on images, text, time series, language data, etc., are included for all architectures, equipping with practical skills to excel in the field of deep learning.

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

Computer Vision

• Industry use cases and applications of computer vision

• Case studies in computer vision

• Latest trends in computer vision

Speech Recognition

• Latest industry use cases and applications of speech recognition

• Case studies in speech recognition

• Latest trends in speech recognition

Natural Language Processing (N

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• VisualML Lab Pro is a no-coding machine learning laboratory platform that enables users to explore AI models through an intuitive visual environment

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• VisualDL Lab is a powerful no-coding deep learning laboratory platform that enables learners to explore neural networks through interactive experimentation and visual analysis

Bring Your Own Project

The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator

• MNIST digit recognition using DNN, CNN, and SVM.

• Classification of real news and fake news using decision trees.

• Prediction of the iris flower species using Naive Bayes classification.

• Classify photos of dogs and cats using Deep Convolutional Neural Network.

• To build a movie recommender model using K-means clustering.

• Identification of IoT devices using experimental radio spectrum dataset and 

• German Traffic Sign Recognition Benchmark: Develop a Feed Forward Neural Network and then a Convolutional Neural Network to classify between the different road signs in the dataset provided. Test it using images from the internet to validate the functioning of your model.

• CIFAR-10 Object Recognition: Develop a Convolutional Neural Network to classify between the different classes of the datasets given below. Test it using images from the internet.

• Sentiment analysis using Naive Bayes Classifier.

• Application of deep learning in medical diagnosis using health data.

• Credit card fraud detection using Random Forest Classifier.

• Music recommendation system using K-NN Algorithm.

• Image compression using K-means Clustering.

This CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL course is suitable for graduates from science or engineering backgrounds seeking a career in the ML/DL domain and for professionals in the software and IT industry seeking to upskill with ML/DL expertise. This course is also suitable for professionals who aspire to work as data engineers, data scientists, machine learning engineers, etc.

Candidates must have a BE/B.Tech/ME/MTech/BIT/MIT/BCA/MCA/MCM (any stream), OR BSc/MSc/BS/MS in Mathematics, Statistics, Electronics, Physics, Computer Science, AI, DS to apply for this CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL.

Secure your spot in the course by clicking the "Enrol Now” button and filling in your details. Our team will swiftly connect with you to provide all the information you need.

The course covers a range of topics such as Foundations of Python Programming, Functional Programming in Python, Data Structures, Loops, and Control Structures, Mathematical Foundations for Machine Learning, Artificial Intelligence Terminologies and Data Analysis, Neural Networks, Fundamentals of Deep Learning, Architectures and Recent Advances, Computer Vision, etc.

The teaching methodology for this CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL course will be highly interactive, leveraging technology, and deploy diverse pedagogical tools and techniques, including lectures, project work, etc.

The CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL is a live course that will be delivered in a direct-to-device (D2D) mode.

Completing the CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL can offer several benefits:

• High-Quality Education: IIT Delhi is renowned for its academic excellence and rigorous curriculum. By enrolling in this certificate programme, you gain access to top-tier faculty members, cutting-edge research, and a comprehensive curriculum designed to impart in-depth knowledge of machine learning and deep learning concepts.

• Prestigious Credential: Obtaining a certificate from IIT Delhi carries significant weight and recognition in the industry. It serves as a validation of your expertise and commitment to mastering machine learning and deep learning techniques, enhancing your credibility and marketability to potential employers.

• Comprehensive Curriculum: The programme covers a wide range of topics relevant to machine learning and deep learning, including algorithms, neural networks, natural language processing, computer vision, and more. This comprehensive curriculum ensures that you acquire a strong foundation in both theoretical principles and practical applications of these technologies.

• Hands-On Experience: The programme likely includes practical exercises, projects, and case studies that provide hands-on experience with real-world datasets and tools used in machine learning and deep learning projects. This practical exposure helps reinforce theoretical concepts and equips you with the skills needed to tackle complex data analysis and modelling tasks.

• Networking Opportunities: IIT Delhi attracts talented students and industry professionals from diverse backgrounds. Participating in this certificate programme allows you to interact with peers, faculty members, and guest speakers who share a passion for machine learning and deep learning. These networking opportunities can lead to valuable collaborations, mentorship, and career connections in the field.

• Career Advancement: With the growing demand for professionals skilled in machine learning and deep learning, completing this certificate programme can significantly enhance your career prospects. Whether you are seeking to advance in your current role, transition to a new career path, or start your own venture in artificial intelligence, the knowledge and credentials gained from this programme can open doors to exciting opportunities in various industries.

The CEP, IIT Delhi Advanced Certificate Programme in AI, ML and DL in India offers a comprehensive curriculum covering key concepts such as supervised and unsupervised learning, neural networks, natural language processing, computer vision and deep learning. With a focus on practical hands-on experience through projects and case studies, the program equips participants with the skills and knowledge required to excel in the rapidly growing fields of machine learning and deep learning. The certification from a prestigious institution like IIT Delhi adds significant value to one's professional profile, opening up opportunities in sectors like technology, finance, healthcare, and more. Overall, this program provides a solid foundation for individuals looking to pursue a career in artificial intelligence and data science.

The CEP, IIT Delhi offers a cutting-edge AI, Machine Learning and Deep Learning course that stands out due to its rigorous academic curriculum, top-notch faculty, and state-of-the-art research facilities. The program emphasises hands-on learning through practical projects and real-world applications, ensuring students gain practical skills alongside theoretical knowledge. Additionally, IIT Delhi's industry collaborations provide valuable networking opportunities and exposure to the latest trends in the field. By choosing IIT Delhi for this course, students can benefit from a holistic learning experience that equips them with the necessary expertise to excel in the rapidly evolving field of artificial intelligence.