Skill Development Workshops Offer By IEEE Bombay Section Year 2019

Introduction

In these workshops students will learn the importance of upcoming technologies such as of Embedded System/ IoT/Machine learning, etc and trends for the future. These workshops are hands-on course especially designs for UG/PG students to develop their hardware and programming skills. These courses will help students to develop their research interest,   Proof of Concept (PoC), and prototyping their ideas.  

Discipline: Electronics, Telecommunication, Computer Science, Process Control, Automation, etc

Workshop Duration: One or Half Day
Requirements:

  • Functional IEEE Students Branch
  • Lab with Wifi Facility

Maximum Registration: 40 Students (As per availability of Kits or Computers)

 Registration Charges: Rs 300 per IEEE Student Member

                                       Rs: 400 per Non-IEEE Student Member

Program Created and Run by: IEEE Bombay Section

Contact Person: Dr.Saurabh Mehta, TPAC , IEEE Bombay Section

                            Email Id: saurabh.mehta@vit.edu.in

Note: Please note that workshop speakers traveling and local hospitality expense need to be supported by  host institute/college

List of Workshop

Sr.No.Workshop Name and Course ContentTrainer No of ParticipantsDuration Requirement from Host
1Hands-on FPGA, VHDL and Softcore Processor Training
-Hands on MAX_10 _Dev Boards

Course Content :
-Introduction to Programmable Logic: From PALs/PLAs to CPLDs to FPGAs
- Software Installation
- Introductory Session on VHDL
-FPGA Tool Flow and Introduction to Altera Quartus
-FPGA Hands-On Session on MAX_10_DEV Boards
Dr.Satyanarayana
Bheesette, TIFR
&
His Team
20/301 DayComputer Lab, Internet Connection, Required Softwares (List will be given in advance by Trainer)
Projector, Sound System, etc
2Workshop on Embedded System/IoT/Arduino Programming

Course Content
-Arduino Programming
-Sensor Integration
-Integrating Communication Module
-IoT Applications using Cloud
Dr.Saurabh Mehta, VIT
&
His Team
401 or Half DayComputer Lab, Internet Connection, Required Softwares (List will be given in advance by Trainer)
Projector, Sound System, etc
3Machin Learning Workshops

1) Data Analysis with Python (~7 hrs)
Python Fundamentals, NumPy, SciPy, Pandas, Pickle, Matplotlib, Seaborn

2) Math for Machine Learning - Linear Algebra and Optimization (~7 hrs)
Linear Algebra: Norms, Moore-Penrose Pseudo-Inverse, Special Matrices, Matrix Rank, Eigenvalues and Eigenvectors, Matrix Decompositions (Cholesky, SVD, etc.), Application: Linear Regression
Optimization: Gradient Descent, Stochastic Gradient Descent, Application: Linear Regression using GD

3) Math for Machine Learning - Statistics and Probability (~6 hrs)
Probability and Statistics concepts useful for Machine Learning, Statistical Hypothesis Testing, Confidence Intervals for evaluation, Application: Anomaly Detection

4) Machine Learning with Python - I (~10 hrs)
ML Concepts, Scikit-learn API, Logistic Regression (LR), Model Evaluation, Stochastic Gradient Classifier (SGD), Model Validation, K-Nearest Neighbors (KNN)

5) Machine Learning with Python - II (~10 hrs)
Naive Bayes (NB), Support Vector Machines (SVM), Support Vector Regression (SVR), Perceptron, Multi-layer Perceptron (MLP)

6) Machine Learning with Python - III (~10 hrs)
Extreme Learning Machine (ELM), Model Comparison with ROC curves, Principal Component Analysis (PCA), t-SNE, K-Means Clustering, Gaussian Mixture Modeling (GMM)

7) Machine Learning Research Topics (~5 hrs)
Recommender System, Sentiment Analysis, Eigen-faces
Prof. Santosh Chapaneri, SFIT
&
His Team
30/407 Hrs to 10 HrsComputer Lab, Internet Connection, Required Softwares (List will be given in advance by Trainer)
Projector, Sound System, etc.
4Deep Learning Workshops:
(Pre-requisites - Beginner level Python programming skills and conceptual understanding of Machine Learning)

1) Lec: Introduction to Deep Learning (~4 hrs)
Understanding of (theory and math) concepts related to DL and CNN

2) Deep Learning for Computer Vision (~7 hrs)
TensorFlow & Keras APIs, Multilayer Perceptron using Keras, Deep Multilayer Perceptron using Keras, Convolutional Neural Networks

3) Advanced Deep Learning (~5 hrs)
AutoEncoders, Transfer Learning
5Workshop on Tinkercad Circuits
-Simulation of various boards on tinkercard software
-helpful in embedded ckt development
Hannan SatopayComputer Lab, Internet Connection, Required Softwares (List will be given in advance by Trainer)
Projector, Sound System, etc.
6Workshop on Amazon Alexa
-How to use Alexa for automation
-Handson on developing Alexa skills
Hannan SatopayComputer Lab, Internet Connection, Required Softwares (List will be given in advance by Trainer)
Projector, Sound System, etc.

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