Please enter your details.The download link would be sent to your email.
Duration: 10 days Timings: 1400hrs -1715hrs (on Saturday and Sunday) Dates: 1-2, 8-9, 15-16 , 22-23, 29-30 May,2021 Programme Director:Prof. Ashok Kumar Harnal & Prof. Sunita Daniel Mode:Online Fees: Rs.20,000 Plus GST @18%
About the program:
Here is a program that your faculty and students may find very useful and at the same time gain substantial knowledge about Machine Learning techniques. It is a no-coding, non-programming but at the same time totally hands-on Program on Machine Learning & AI Techniques (ML & AI). It is an online, live, and totally interactive lab oriented program with the primary objective of disseminating techniques of Analytics using Machine Learning to enabling one to apply them on data in one's research work or for teaching or in any industrial application. The program duration is 30+ hours.
About KNIME:
We use KNIME to implement ML & AI Techniques. KNIME is to Data Science what SPSS is to Statistics. Just as in SPSS, it is easy to import data, analyse it and generate reports for statistical analysis, so also it is equally easy (if not easier) to implement ML&AI techniques and publish results using KNIME--no matter how small or how large your dataset is. Also, just as results from SPSS are recognised widely in research community, so also KNIME's credibility to Data Science is recognised the World over and for the sixth year in a row, Gartner has placed KNIME as a leader for Data Science and Machine Learning (ML) Platforms in its Magic Quadrant based on ability to execute and completeness of vision. See links here and here. KNIME also offers an additional bonus for those who are familiar with R. They can extend its utility very easily. This facility is in parallel with SPSS which also offers facility for programming. KNIME is being used extensively in Industries for research & production oriented work. Please see this example link for KNIME related jobs.
H2O:
We also use another top-of-industry tool--H2O.ai. Both KNIME and H2O are open-source platforms, free to download and use (GPL ver 3 licence). It is very easy to install on one's laptop. They are installable on Windows, Mac or Linux platform (for example Ubuntu).
Primary Objectives of the Program:
Develop insights into data through visual analytics
To discover if data has any structure
To learn techniques to group/cluster data
To develop models for predictive analytics
To optimize model performance, and
To discover attributes that contribute most towards higher performance-->Explainable AI
Program Contents:
We practice techniques, which consistently garner high performance and are well known in ML community. Thus, these will be of immense use any predictive application:
Introduction to Machine Learning Technology
Data visualization (including t-sne, parallel coordinates, mosaic plots) and Feature importance
Unsupervised learning techniques
K-means clustering
Hierarchical clustering
Expectation-Maximization algorithm
T-SNE manifold learning technique
Dimensionality reduction
Principal Component Analysis (PCA)
Kernel PCA
Supervised learning techniques for Classification and Regression
Decision trees
Ensemble modelling using Random Forest
Gradient Boosting Techniques
Gradient Boosting Learner
XGBoost
LightGBM
Handling imbalanced data—SMOTE and ADASYN
Performance measures: Accuracy, Precision and Recall, F-measure; Area Under the Curve, Cohen’s Kappa, Sensitivity, Specificity
Deep Learning Techniques
Neural Networks
Deep Learning models using H2O
Anomaly detection
Anomaly detection using isolation forests
PCA with Mahalanobis distance
Autoencoders
Automated Machine Learning (AML) using H2O
Methodology:
We strongly believe that a course in data analytics can only be practice-based rather than theory based. We also believe that a practice based course requires constant interaction with the teacher during lecture hours in real time. As it is an online course, the teaching pedagogy is like this: First the algorithm (or theory part) is conceptually explained without getting into mathematics and then a project is undertaken to implement the techniques. Datasets for implementation are made available in advance. During the lecture, we create workflow on KNIME and explain the steps. At his end, the student goes through the same steps on his laptop. Consequently, results are available at our end as also with the Students immediately. In short, both the teacher and students are working on their respective laptops simultaneously; students solve their problems and ask any questions to clarify. The whole experience is just as if everyone is sitting in a laboratory and working together. Students are required to have a laptop with minimum of 4GB of RAM.
Students will be given practical exercises (along with data) on every topic and they are expected to submit them for evaluation within the time prescribed---Learning-by-exercises to model with real data constitutes an important component of our program.
Target Organizations:
This program is ideally suited for product and services companies where the core business is in Artificial Intelligence or are using Artificial Intelligence for analytics and decision making. It is also useful for executives of marketing and market research companies.
Target Participants:
Today no field or discipline remains untouched by data. Most organizations today are driven by data. This program would, therefore, be useful to anyone who would like to advance his career growth opportunities or add value to his organization or do something useful for society or aspires to be a data scientist. In short, whether you are a teacher, Ph.d scholar, student (of any discipline), or a working professional--this program is for you.
Program Faculty:
Prof. Ashok Kumar Harnal
Prof. Ashok Kumar Harnal: Graduated from IIT Delhi in Electronics and Communication, Expert in Big Data and Data Analytics. Extensively taught faculty and students on the subject of big data technology and analytics. Participated in various machine learning competitions with real world data in areas of business, environment, marketing and advertisement. Have set up fully functional Big-data laboratory. Long experience in working with Opens Source Systems. Have published two books: one Linux Applications and Administration and the other Techniques of Game Programming; both published by Tata McGrawHill. Conceived, planned & implemented in Defence Estates three country-wide information systems: Raksha Bhoomi to computerize land records; Knowledge Management of landtitle related files/ maps in all Defence Estates offices; and Setting up of a Disaster Management organization, Archival Unit and Resource Center, at Delhi for safe storage of land-title related records in paper, digital & microfilm forms.
Prof Sunita Daniel
Prof. Sunita Daniel, Associate Professor in IT Area has done her PhD in Mathematics from IIT Kanpur. She has been into teaching and research for more than 20 years. She has presented research papers in international conferences and published several research papers in highly ranked national and International journals.
She is at present guiding PhD students and has taught various subjects such as financial modelling, biostatistics, statistical quality control etc. His present areas of research are Big Data and Big Data Analytics, Computer Aided Geometric Design and Mathematical Modelling of Non-Linear Dynamical Systems
Corporate Group Discount:
One complimentary nomination for every group of three nominations from the same organization, i.e., 3+1 participants for the fee of 3 participants
For registration/enquiries , please mail to exed@fsm.ac.in or call at 9166085159 (Mukesh) or 9810875278 (Puneet)
Enquiry Form
Please enter your details to enquiry.
Executive Education/MDPs
FORE School of Management has been designing, developing and conducting innovative Executive Education (EE)/ Management Development Programmes (MDPs) for working executives in India for over three decades.
