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PG Diploma in Artificial Intelligence



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The objective of this course is to impart in depth knowledge in Artificial Intelligence and its applications using tools and case studies thereby empowering students to use computational techniques in the area of Artificial Intelligence, Natural Language Processing, Machine Learning and Deep Learning based applications
  • Graduate in Engineering (10+2+4 or 10+3+3 years) in IT / Computer Science / Electronics / Telecommunications / Electrical / Instrumentation. OR MSc/MS (10+2+3+2 years) in Computer Science, IT, Electronics. 
  • Mathematics in 10+2 (exempted for candidates with Diploma + Engineering) OR
  • Post Graduate Degree in Mathematics/ Statistics/ Physics, OR
  • MCA
  • The candidate must have 60% in the qualifying degree.
The total fees of the course is Rs. 1,50,000/- plus Goods and Service Tax (GST) currently 18%.

The course fees has to be paid in two installment as per the schedule.
  • First installment is Rs. 10,000/- plus Goods and Service Tax (GST) currently 18%.
  • Second installment is Rs. 1,40,000/- plus Goods and Service Tax (GST) currently 18%.
  

Mathematics

a. Probability

Basic rules and axioms, events, sample space, frequentist approach, dependent and independent events, conditional probability, Random variables, continuous and discrete, expectation, variance, distributions- joint and conditional, Bayes’ Theorem, MAP, MLE, Popular distributions- binomial, bernoulli, poisson, exponential, Gaussian, Conjugate priors

b. Linear Algebra

  • Vectors, definition, scalars, addition, scalar multiplication, inner      product (dot product), vector projection, cosine similarity,     orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, 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, popular type of matrices- symmetric, diagonal, orthogonal, orthonormal, positive definite matrix
  • Eigenvalues & eigenvectors, concept, intuition, significance, how to find Principle component analysis, concept, properties, applications
  • Singular value decomposition, concept, properties, applications

c. Calculus

  • Functions, 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


d. Statistics:

Descriptive Statistics, Summary Statistics Basic probability theory, Statistical Concepts (uni-variate and bi-variate  sampling, distributions, re-sampling, statistical Inference, prediction error), Probability Distribution(Continuous and discrete- Normal, Bernoulli, Binomial, Negative Binomial, Geometric and Poisson  distribution),Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation, Regression,      Covariance, Outliers etc.

e.  Miscellaneous Topics

  • Information theory, entropy, cross entropy, KL divergence, mutual information
  • Markov Chain, definition, transition matrix, stationarity.
  

R Programming: Introduction & Installation of R, R Basics, Finding Help, Code Editors for R, Command Packages, Manipulating and Processing Data in R, Reading and Getting Data into R, Exporting Data from R, Data Objects, Data Types & Data Structure. Viewing Named Objects, Structure of Data Items, Manipulating and Processing Data in R (Creating, Accessing , Sorting data frames, Extracting, Combining, Merging, reshaping data frames), Control Structures, Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects, Constructing Data Objects, Building R Packages, Running and Manipulating Packages, Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test, Introduction to Graphical Analysis, Using Plots(Box Plots, Scatter plot, Pie Charts, Bar charts, Line Chart), Plotting variables, Designing Special Plots, Simple Liner Regression, Multiple Regression, Interactive reporting with R markdown.

 

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, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, Python Libraries, Data migration and visualization: Pandas and Matplotlib, Database Interaction in Python 

Case Studies: 

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

Data Analytics

80 Hours  
  

Introduction to Data Analytics, Descriptive Statistical Measures, Probability Distribution and Data, Sampling and Estimation, Predictive modelling and analysis, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Non linear, Integer, Decision Analysis, Making Right Business Decisions based on data, Exploratory Data Analysis, Visualization and Exploring Data, Text analytics, Social network analysis, web scrapping, Dimensionality issues, Ridge & lasso regression, bias/variance trade off, density, PCA, FA, Directional Data Analytics, Functional Data Analysis, Data Analysis & visualization using numpy, matplotlib, scipy, Advanced python packages

  

Introduction to AI, Evolution & Revolution of AI, Introduction to AI, Introduction of Applications in various Domains (Scientific including Health Sciences, Engineering, Financial Services and other industries), 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 Behaviour, Genetic Algorithms

Machine Learning

100 Hours  
  

Introduction  to  machine  learning   and   need,    The Learning Problem, Terminology, Canonical Learning Problems,                                        Supervised          Learning,          Unsupervised Learning, Reinforcement Learning, ML applications in the real world,  A key ML concept, Uses  of Machine learning , Introduction to feature engineering, raw data to feature, Data Preparation , feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, PCA, Ensemble methods, Bagging & Boosting , ML Algorithms, Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes  classifier,  Support  vector  Machines  ,  KNN,  Gradient  boosting  ,  Association  rules learning, Apriori and FP growth algorithms, Linear and Non liner classification, linear and logistic Regression, Clustering ,K-means ,Overview of Factor Analysis, ARIMA, ML in real time , Algorithm performance metrics , ROC ,AOC, Confusion matrix , F1 score, MSE, MAE. 

Machine Learning Tools: Introduction to the basic data science toolset

Case Studies:

  •  Usage          of          ML          algorithms,         Algorithm           performance          metrics (confusion matrix sensitivity, specivity, ROC, AOC, F1score, Precision,Recall, MSE, MAE)
  • Implementation of case studies will be using R / Weka.
  • Implementation of ML algorithms using high level api like Scikit-learn.
  •  Credit Card Fraud Analysis , Intrusion Detection system,
  • Implement basic gradient descent in Tensor Flow.

  

Introduction to Deep Neural Network, RNN, CNN ,LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, introduction to Tensorflow, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (Alog model), Transfer Learning, Inductive, unsupervised Transductive, Deep Learning Tools & Technique

  

Introduction to reinforcement learning as an approximate dynamic programming problem, Overview of reinforcement learning: the agent environment framework, successes of reinforcement learning, Bandit problems and online learning, Markov decision processes, Returns, and value functions, Solution methods: dynamic programming, Solution methods for learning, Solution methods for temporal difference learning, Eligibility traces, Value function approximation Models and planning (table lookup case) 

Case studies: Successful examples of RL systems, simulation based methods like Q-learning.

  

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, Psycholinguistics, Neurolinguistics, Computational Linguistics, Sociolinguistics etc.

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), Speech Processing, Machine Vision, NLP Applications, Language identification, Auto suggest/ Auto complete, Gender Prediction, Face Recognition, Robotics, chat bots.

  

Parallel Python, Application of Multi Processing in DL, Spark Platform, Python on Spark (PySpark), Machine Learning with PySpark,  Deep Learning with   PySpark, Sample implementation, Tuning Deep Learning Models, Trends in Deep Learning, Deep Learning Case Studies, Architecture Details for Compute efficiency, Parallel Programming models, Hardware for AI. Various new CPU aspects and optimizations, Parallel Programming aspects. Compiler optimizations, Multi-thread Programming, Multi-process Programming, AI Distributed Learning, OpenVino Optimization for AI Networks, Python, Intel Distributions of Python, Tensorflow, Deployment of Models on distributed platform.AI latest trends and future.

  

   .

Project

120 Hours  
  
Along with project following module will be conducted.

Artificial Intelligence in Production (20hrs)
Deployment & Maintenance of AI Applications, AI application testing, AI model, interoperability, problem solving approaches.

Case studies:
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.

PG Diploma in Artificial Intelligence (PG-DAI) comprehensive programme that combines Data Science, Machine Learning and Deep Learning to prepare candidates for the roles of Applied AI Scientists, Applied AI engineers, AI architects, Technology architects, Solution Engineers, Technology Consultants.

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
C-DAC Advanced Computing Training School
Address
:
C-DAC Innovation Park Sr. No. 34/B/1 Panchvati, Pashan Pune
Maharashtra 411008
Telephone
:
18008430222
Contact Person
:
Mr. Parimal Wagh
Fax
:
NA
e-Mail
:
acts[at]cdac[dot]in
Courses
:
PG-DAC, PG-DVLSI, PG-DESD, PG-DITISS, PG-DAI, PG-DBDA, PG-DIoT, PG-DHPCSA