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Diploma in Artificial Intelligence (e-DAI)



The objective of the e-DAI course is to present in-depth knowledge and applications in Artificial Intelligence using tools and case studies. Upon completion of this course, participants will be empowered to use computational techniques in the area of Artificial Intelligence, Natural Language Processing, Machine Learning and Deep Learning based applications.
  • Graduate in Engineering in IT, Computer Science, Electronics, Telecommunications, Electrical, Instrumentation, OR
  • Post Graduate Degree in Computer Science, IT, Electronics, Mathematics, Statistics, Physics, OR
  • MCA
Rs 80,000 + GST
  

Introduction to AI, Evolution & Revolution of AI, Ethics of AI, Structure of AI, Real world Implications, Intelligent Agents, Uninformed Search, Constraint Satisfaction Search, Combinatorial Optimization Problems, Heuristic & Meta-heuristics, Genetic Algorithms for Search, Game Trees, Supervised & Unsupervised Learning, Knowledge Representation, Propositional and Predicate Logic, Inference and Resolution for Problem Solving, Rules and Expert Systems, Artificial Life, Emergent Behavior, Genetic Algorithms

  
Linear Algebra
Vectors, definition, scalars, addition, scalar multiplication, inner product(dot   product), vector projection, cosine similarity, orthogonal vectors, normal and ortho-  normal vectors, vector norm, vectors pace, linear combination, linear span, linear   independence, basis vectors
Matrices definition, addition, transpose, scalar multiplication, matrix multiplication,   matrix multiplication properties, hadamard product, functions, linear transformation,   determinant, identity matrix, invertible matrix and inverse, rank, trace, popularity of   matrices-symmetric, diagonal, orthogonal, ortho-normal, positive definite matrix
Eigen values & eigen vectors, concept, intuition, significance, how to find Principle   component analysis, concept, properties, applications
Singular value decomposition, concept, properties, applications

Calculus:
Function scalar derivative, definition, intuition, common rules of differentiation,   chain rule, partial derivatives, Gradient, concept, intuition, properties, directional   derivative
Vector and matrix calculus, how to find derivative of scalar-valued, vector-valued   function with respect to scalar, vector}four combinations- Jacobian
Gradient algorithms, local/global maxima and minima, saddle point, convex functions,   gradient descent algorithms-batch, mini-batch, stochastic, their performance comparison
  
Python Programming: Introduction to Python, Basic Syntax, Data Types, Variables, Operators, Input/output, Flow of Control (Modules, Branching), If, If-else, Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Introduction, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions and Functional Programming, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, matplotlib, seaborn,
Advanced Python: Object Oriented, OOPs concept, Class and object, Decorators, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, Python Libraries, Web based frameworks: Flask and Django

Self Study: Mathematical computing with Python, Data migration and visualization: Pandas and Matplotlib, Pycharm, Anaconda, Data manipulation with Pandas

Data Analytics

80 Hours  
  

Introduction to Business Analytics using some case studies, Summary Statistics, Making Right Business Decisions based on data, Statistical Concepts, Descriptive Statistics and its measures, Probability theory, Probability Distributions (Continuous and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and Estimation, Statistical Interfaces, Predictive modeling and analysis, Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation,  Covariance, Outliers, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Nonlinear, Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data Analysis , Hypothesis Techniques, Predictive Modelling (From Correlation To Supervised Segmentation): Identifying Informative Attributes, Segmenting Data By Progressive Attributive, Models, Induction And Prediction, Supervised Segmentation, Visualizing Segmentations, Trees As Set Of Rules, Probability Estimation; Overfitting And Its Avoidance, Generalization, Holdout Evaluation Vs Cross Validation; Decision Analytics: Evaluating Classifiers, Analytical Framework, Evaluation, Baseline, Performance And Implications For Investments In Data; Evidence And Probabilities: Explicit Evidence Combination With Bayes Rule, Probabilistic Reasoning;

Python Libraries – Pandas, Numpy, Scipy

  

Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world

Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA

ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting , Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System

Self Study: Usage of ML algorithms, Algorithm performance metrics(confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE)

Credit Card Fraud Analysis, Intrusion Detection system

  

Introduction to Deep Neural Network, RNN, CNN, LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, Tensorflow 2.x, Pytorch, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (A log model), Transfer Learning, Inductive, unsupervised Transductive, Deep Learning Tools & Technique, Tuning Deep Learning Models, Trends in Deep Learning, Application of  Multi Processing in DL, Deep Learning Case Studies

  

Natural Language Processing: Understanding Language, NLP Overview, Introduction to Language Computing, Language in Cognitive Science, Definitions of language, Language as a rule-governed dynamic system, Language and symbolic systems: Artificial language (Logical language / programming language) vs. Natural Language, Linguistics as a scientific study, And Description of different branches of Linguistics: Statistical Linguistics, Psycho linguistics, Neuro Linguistics, Computational Linguistics, Socio linguistic, Language Analysis and Computational Linguistics, Semantics, Discourse, Pragmatics, Lexicology, Shallow Parsing and Tools for NLP, Deep Parsing and Tools for NLP, Statistical Approaches, NLP with Machine Learning and Deep Learning, Pre-processing, Need of Pre-processing Data, Introduction to NLTK, Using Python Scripts

Word2Vec models(Skip-gram, CBOW, Glove, one hot Encoding), NLP Transformers, Bert in NLP Speech Processing, NLP Model Deployment Techniques using Flask, NLP Applications- Language identification, Auto suggest/ Auto complete, chat bots, Robotics

Computer Vision: Introduction to Computer Vision, Computer Vision and Natural Language Processing, The Three R's of Computer Vision, Basics of Image Processing, Low-, Mid- & High-Level Vision, Edge Detection, Interest Points and Corners, Image Classification, Recognition, Bag of Features, and Large-scale Instance Recognition, Object Detection & Transfer Learning, AlexNet, ResNet, Image Net, Gender Prediction, Face / Object Recognition

  

Apache Spark APIs for large-scale data processing: Basics of Spark, Deploying to a Cluster Spark Streaming, Spark ML lib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Connecting DB’s with Spark,

AI Compute Platforms: Deep Vision, Cloud Machine Learning Engine, Tensorflow AI Network optimization using Intel Open Vino Toolkit

AI Future Trends

Self Study: AI applications in Financial Services including Insurance banking, stock markets & other financial markets like Forex–and Artificial Economics, AI applications in Health Sciences & other Scientific Applications, AI in Cloud Environment. Deployment of Models on distributed platform.

Project

80 Hours  
   .

Table: Syllabus and reference books for the C-CAT in August 2020.

SYLLABUSREFERENCE BOOK
EnglishAny High School Grammar Book (e.g. Wren & Martin)
Quantitative Aptitude & ReasoningQuantitative Aptitude Fully Solved (R. S. Aggrawal)
Quantitative Aptitude (M Tyara)
Barron’s New GRE 2016
Computer FundamentalsFoundations of Computing (Pradeep Sinha & Priti Sinha)
C ProgrammingC Programming Language (Kernighan & Ritchie)
Let Us C (Yashavant Kanetkar)
Data StructuresData Structures Through C in Depth (S. K. Srivastava)
OOP ConceptsTest Your C ++ Skills (Yashavant Kanetkar)
C-DACs - Advanced Computing Training School
Address
:
B-30, Sector 62, Institutional Area, Noida
Uttar Pradesh 201307
Telephone
:
0120-3063371-73
Contact Person
:
Mr. V.K. Sharma
Fax
:
0120-3063374
e-Mail
:
cdacacts-noida[at]cdac[dot]in
Courses
:
PG-DAC, PG-DVLSI, PG-DGi, PG-DESD, PG-DMC, PG-DITISS, PG-DAI, PG-DBDA, PG-DIoT, e-DAC, e-DBDA, e-DAI