KIT Online

FDP on Machine Learning

Batch A - Part I
09 - 14 November 2020
   Batch A - Part II
15 - 20 November 2020
   Batch B -Part I
23 - 28 November 2020
   Batch B -Part II
29 November - 04 December 2020

Objectives

This course aims to provide a concise introduction to the fundamental concepts in machine learning including mathematical foundations, programming tools and packages and popular machine learning algorithms. The participants will gain knowledge in Machine Learning principles through a lot of practical applications covering industrial case walk-throughs and real-time applications.

Topics to be discussed

Sr. No. Topic Sub Topic Theory (Hours) LAB (Hours)
Welcome to Machine Learning Introduction To Machine Learning 2 1
Introduction of Data Science, ML and AI
Concept of Data Science - What/When/Why
Artificial Intelligence Evolution
Find out where Machine Learning is applied in Technology and Science. Applications in real scenarios
GUI Based Machine Learning Development
Basics of Python Basic programming syntax of Python, File Handling 2 3
Fundamental of Machine Learning Categories Supervised Learning 1
Unsupervised Learning
Reinforcement Learning
Working with Data Reading Files, Scraping the Web, Cleaning, Munging and Manipulating 2 3
Visualizing Data Bar Charts, Line Charts, Scatterplots 1 1
Statistics:Describing a Single Set of Data Correlation and Causation 1
Mathematical Foundations Dependence and Independence, Conditional Probability, Bayes’s Theorem, Random Variables, Continuous Distributions, The Normal Distribution 3
Machine Learning Python Packages Data Analysis Packages 3 3
NumPy
Matplotlib
Pandas
Sklearn
Supervised Machine Learning Regression 1
Classification
Generalization, Overfitting and Underfitting
UnSupervised Machine Learning Challenges in unsupervised learning 1
Supervised Machine Learning Algorithms Linear models 2 3
k-Nearest Neighbor
Naive Bayes Classifiers
Decision trees
Supervise ML - Classification Classification - LogisticRegression, Sigmoid Function 1 1
Bagging and Boosting Random Forest 1 1
Supervised ML - Regression Code a Linear Regression in Python with scikit-learn 1 2
UnSupervise ML - Clustering k-Means clustering 1 2
Introduction Neural Networks Genearl introduction of Neural Networks-Learning And Generalization, Overview of Deep Learning 1 2
Prepare Your Data For Machine Learning Need For Data Pre-processing 1 3
Data Transforms
Rescale Data
Standardize Data
Normalize Data
Binarize Data
Feature Engineering
Feature Selection
Evaluate the Performance of Machine Learning Algorithms with Resampling Evaluate Machine Learning Algorithms 1 3
Split into Train and Test Sets
K-fold Cross Validation
Leave One Out Cross Validation
Repeated Random Test-Train Splits
Understand different error metrics such as MSE and MAE in the context of Machine Learning.
Practice Advice on Which Techniques to Use and When for specific scenarios
Examples of Real-time application of ML Weather forecasting, Stock market prediction, Object recognition, Real Time Sentiment Analysis, etc. 1 1
Industrial use case walk through Industrial use case walk through for supervised learning 3 1
Industrial use case walk through for unsupervised learning

Coordinator


Dr. Amey Karkare
(Associate Professor, Dept. of Computer Science and Engineering)

karkare@cse.iitk.ac.in
https://www.cse.iitk.ac.in/users/karkare/

Registration

Registeration will be done by nomination. The nominations should be sent by the TEQIP coordinator/Director/HOD of the institute/department. There is no registration fees for participants from TEQIP institutes.

The course will be held in two batches.
Batch A: November 09 – November 20
Batch B: November 23 – December 04
Please mention which batch the nominated faculty will attend.

We look forward to enthusiastic participation from your esteemed institute. TEQIP Coordinator/Head of the departments of the institute are requested to send the nomination for this course in the following format:


S.No Applicant’s Name Batch (A/B) Designation Department Email Mobile
1.
2.
3.
4.
5.


Please email these details to teqip.iitk@gmail.com, with subject “Machine learning 2020”. There are limited seats, please send your registration as soon as possible.

Selection will be made on first-come-first-serve basis. If selected, you will be sent a confirmation email. Your expression of interest in participation is confirmation of your participation unless communicated otherwise.

Speakers

Sr. No. Name
1. Dr. Amey Karkare, CSE, IIT Kanpur
2. Dr. Arnab Bhattacharya, CSE, IIT Kanpur
3. Industry Talks , (Algo8, RCPL, Google Research)

selected participants

Batch A (09 - 20 November 2020 )
Sr. No. Name Institute Name Sr. No. Name Institute Name
Prof. Annappa NIT Karnataka, Surathkal Mr. R. Marx PSG College of Technology, Coimbatore
Ms. G. Sindhu PSG College of Technology, Coimbatore Dr. Subrajeet Mohaptra BIT Mesra
Sudip Kumar Sahana BIT Mesra Shamama Anwar BIT Mesra
Amitava Dey GUIST, Gauhati University, Assam Jyoti Prakash Medhi GUIST, Gauhati University, Assam
Kishore Kashyap GUIST, Gauhati University, Assam Madhurjya Modhur Borgohain GUIST, Gauhati University, Assam
Eeshankur Saikia GUIST, Gauhati University, Assam Prem Shanker Yadav IET Ayodhya
Rajesh Kumar Singh IET Ayodhya Shambhavi Shukla IET Ayodhya
Rajnish Pandey IET Ayodhya Avadhesh Kumar Dixit IET Ayodhya
Praveen Mishra IET Ayodhya Bijit Kumar Das IIIT Guwahati
Anuradha jha IIIT Guwahati Sounak Roy IIIT Guwahati
Dr Ragini Meshram VJTI Mumbai Dr. Prashant Bhopale VJTI Mumbai
Dr. Vivek Kachatiya VJTI Mumbai Dr. Sowmya Gupta VJTI Mumbai
Dr. Chinmay Rajhans VJTI Mumbai Dr Ananda babu J Malnad College of Engg., Hassan Karnataka
Mr.T.S.Vishnu Kumar Sri Venkateswara University College of Engineering Tirupati Dr. Dayanand UCET, VBU, Hazaribag.
Arun Kumar M.L.V. Textile & Engineering College Rohit Negi M.L.V. Textile & Engineering College
Dr. Krishnan CMC NIT Surathkal Dr (Mrs.) Amba Shetty NIT Surathkal


Batch B (23 November - 04 December 2020)
Sr. No. Name Institute Name Sr. No. Name Institute Name
Ms. Sasmita Behera VSSUT, Burla Ms. Nutan Saha VSSUT, Burla
Dr. Sanjay Agrawal VSSUT, Burla Dr. Sumanta Panda VSSUT, Burla
Mr. Lingraj Dora VSSUT, Burla Dr. G. Subashini PSG College of Technology, Coimbatore
Dr. B. Malar PSG College of Technology, Coimbatore Mr. S. Sainath PSG College of Technology, Coimbatore
Abhijit Mustafi BIT Mesra Debjani Mustafi BIT Mesra
Sujan Kumar Saha BIT Mesra Ritesh Jha BIT Mesra
Vandana Bhattacharjee BIT Mesra Radhika Sukapuram IIIT Guwahati
Rohit Tripathi IIIT Guwahati Dr.Md Jawaid Alam VJTI Mumbai
Prof H B Choudhary VJTI Mumbai Prof Vaibhav Dhore VJTI Mumbai
Prof (Ms) Mansi Kulkarni VJTI Mumbai Prof (Ms) Shraddha S. Suratkar VJTI Mumbai
Dr. S S Udmale VJTI Mumbai Dr. Srinivas Sethi IGIT Sarang Odisha
Dr. Kesari Verma NIT Raipur Shailendra Kumar Sonkar UCET, VBU, Hazaribag
Dr. Manish Kumar Jha UCET, VBU, Hazaribag Rabel Guharoy UCET, VBU, Hazaribag
Susobhan Das UCET, VBU, Hazaribag Mrs. Neeraj Choudhary UCET Bikaner
Mrs. Sunita Choudhary UCET Bikaner Arun Kumar Mishra UCET, VBU, Hazaribag
Dr (Mrs) A Sathyabhama NIT Surathkal Dr. Basavaraj Talwar NIT Surathkal
Dr Chinta Sankara Rao NIT Surathkal Dr Bharadwaj Nanda VSSUT, BURLA
Dr. Siva Kumar Tadepalli NIT Uttarakhand Mr. Shahid Husain Zakir Husain College of Engineering & Technology Aligarh Muslim University Aligarh
Dr. Bikesh Kumar Singh NIT Raipur Parthasarathi S Thiagarajar College of Engineering
Dr. Abhyarthana Pattanaik Government College of Engineering, Keonjhar Satish Kumar Ray Engineering College Ajmer
Dr. Kedarnath Senapati National Institute of Technology Karnataka, Surathkal H.Ramesh Thiagarajar College of Engineering
Dr. Kumar Rajnish BIT Mesra Dr. Niraj Singh BIT Mesra
Dr.A.M.Abirami Thiagarajar College of Engineering Dr. Dalchand Jharia National Institute of Technology Raipur C.G.
Dr. Jidesh National Institute of Technology Karnataka, Surathkal

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