Course Objective
Prepare students to use the integration of Embedded Systems and Artificial Intelligence to resolve challenges in industrial systems. Students will learn how incorporating machine learning and deep learning algorithms with embedded systems can streamline processes and automate tasks. Focus will be on developing system approach through integrated projects to master specific methods and tools applied in the aeronautics, space, automobiles, and multimedia domains. The course will impart an in-depth knowledge focussing on both theoretical and practical aspects.
Fee Structure
The course comprises of 6 modules mentioned under the Course Syllabus below. Student can enrol for the full course (6 modules) or is free to enrol for individual modules.
Registration Fee: Rs.500/-
Full Course Fees: Rs.94500/-
Each Module Course Fees: Rs.17500/-
Registration fees and course fees
should to be paid online or by Demand Draft.
Online Payment Details: Account Name – ICIT PVT LTD
Account Number: 27205000514
IFSC Code: SCBL0036107
Bank: Standard Chartered Bank
Bank Branch: Aundh Branch, 163, Harsh Orchid, New DP Road, Nagras RoadMall, Ward no 8, Aundh, Pune 411007
Demand Draft: To be drawn on any nationalized bank in favour of “ICIT Pvt. Ltd., Pune” and should
be payable at par.
Note that registration and course fees once paid are non refundable.
Advanced SQL
- Complex Queries and Subqueries
- Advanced JOIN Operations
- Window Functions
- Common Table Expressions (CTEs)
- Recursive Queries
- Indexing and Performance Tuning
- Stored Procedures and Functions
- Triggers and Events
- Dynamic SQL
- SQL Security Measures
- Transaction Management and Concurrency Control
- Handling Large Datasets
- SQL with JSON and XML
- Integrating SQL with Other Programming Languages
Time Series
- Introduction to Time Series Analysis
- Components of Time Series
- Time Series Data Collection and Preparation
- Moving Averages
- Exponential Smoothing
- ARIMA Models
- Seasonal Adjustments
- Trend Analysis
- Cyclical and Irregular Components
- Forecasting Techniques
- Model Diagnostics and Validation
- Spectral Analysis
- Multivariate Time Series Analysis
Data Analysis using R Programming
- Introduction to R
- R Studio Interface
- Data Structures in R (Vectors, Lists, Data Frames, Matrices)
- Data Import and Export in R
- Data Manipulation with dplyr
- Data Cleaning Techniques
- Exploratory Data Analysis in R
- Statistical Analysis with R
- Regression Analysis
- Classification Techniques
- Data Visualization with ggplot2
Advanced Machine Learning
- Ensemble Methods
- Neural Networks
- Unsupervised Learning
- Reinforcement Learning
- Advanced Regression Techniques
- Support Vector Machines
- Natural Language Processing
- Generative Models
- Graphical Models
- Advanced Optimization Techniques
- Model Evaluation and Hyperparameter Tuning
- Advanced Feature Engineering
- Model Interpretability and Explainability
- Deployment of Machine Learning Models
