VLSI Design & Applications of Machine Learning
Very large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining millions of MOS transistors onto a single chip. The electronics industry has achieved a phenomenal growth over the last few decades, mainly due to the rapid advances in large scale integration technologies and system design applications. This training program will grow the interest of candidates in VLSI technology and will enhance their technical skills for long term professional growth.
Machine Learning is one of the most popular sub-fields of Artificial Intelligence. Machine learning concepts are used almost everywhere, such as healthcare, finance, infrastructure, marketing, self-driving cars, recommendation systems, chatbots, social sites, gaming, cyber security, and many more.
The objective of this course is to present recent trends and applications of machine learning. This course would provide participants with the guidelines to explore the area of Machine Learning and its Application. The participants would learn to develop methods for solving problems related to diverse computational fields. This program will unlock the potential of participants and introduce various skills that go into Machine Learning, provide practical walk-through of relevant languages, tools and lay down study plan for moving forward in this field.
Outline of course
A. VLSI – an introduction
1. History of IC design. 2. Basics of VLSI design. 3. Digital circuit design using CMOS. 4. Overview of ASICs. 5. Introduction to PLDs. 6. Design flow using Xilinx ISE series software.
B. Designing with Schematic
1. Digital circuit design using Schematic. 2. Testing digital circuits on software and hardware. 3. Implementing circuits on programmable chips.
C. Designing with HDL (hardware description language)
1. Introduction to VHDL. 2. Structural modelling. 3. Dataflow modelling. 4. Behavioural modelling.
5. Designing circuit with VHDL.
D. Simulation and synthesis issues
1. Fundamental of simulation and synthesis. 2. Simulation process and types of simulation and simulator.3. Synthesis design flow. 4. Synthesis tools features.
E. Programming of digital circuits using Xilinx
F. Machine Learning - Introduction
a. History of ML b. Types of ML c. Algorithms used in ML d. Applications of ML
G. Supervised Machine Learning
a. Classification b. Regression Analysis c. Applications of Supervised ML Algorithms
H. Unsupervised Learning
a. Clustering b. Association c. Applications of Unsupervised ML Algorithms
I. Neural Networks
a. Deep Forward Neural Network b. Convolutional Neural Network c. Recurrent Neural
Network d. Case Study
J. Real time Machine Learning Projects
a. Introduction to Programming Language b. Rainfall Prediction using Regression (Project I)
c. Fraud Detection (Project II)
Dr. Uday Panwar
(Trainer 1)
Dr Uday Panwar
Professor , Dept of EC SIRT
Convener EDC
Prof. Meha Shrivastava
(Trainer 2)
Prof. Meha Shrivastava
Asst. Professor
Dept of EC
SIRT
Registration Process
1. For the candidates who are registering for the first time: Register yourself by the ensuing form.
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3. Your registered e-mail ID is your USERNAME whereas; Your registered mobile number is your PASSWORD.
4. Apply for any course of your choice by selecting from dashboard, make online payment from the Pay Now button and you are through.
5. You will receive a confirmation mail on your registered email ID.