Course Name: Applied Optimization for Wireless, Machine Learning, Big Data

Course abstract

This course is focused on developing the fundamental tools/ techniques in modern optimization as well as illustrating their applications in diverse fields such as Wireless Communication, Signal Processing, Machine Learning, Big-Data and Finance. Various topics will be covered in different areas such as; Wireless: MIMO/ OFDM systems, Beamforming, Cognitive Radio and Cooperative Communication; Signal Processing: Signal Estimation, Regularization, Image Reconstruction; Compressive Sensing: Sparse estimation, OMP, LASSO techniques; Machine Learning: Principal Component Analysis (PCA), Support Vector Machines (SVM); Big-Data: Recommender systems, User-rating prediction, Latent Factor Method; Finance: Financial models, Portfolio Optimization. The course is suitable for all UG/PG students and practicing engineers/ scientists/ managers from the diverse fields mentioned above and interested in learning about the novel cutting edge applications of modern optimization technology.


Course Instructor

Media Object

Prof. Aditya K. Jagannatham

Prof. Aditya K. Jagannatham received his Bachelors degree from the Indian Institute of Technology, Bombay and M.S. and Ph.D. degrees from the University of California, San Diego, U.S.A.. From April 07 to May 09 he was employed as a senior wireless systems engineer at Qualcomm Inc., San Diego, California, where he worked on developing 3G UMTS/WCDMA/HSDPA mobile chipsets as part of the Qualcomm CDMA technologies division. His research interests are in the area of next-generation wireless communications and networking, sensor and ad-hoc networks, digital video processing for wireless systems, wireless 3G/4G cellular standards and CDMA/OFDM/MIMO wireless technologies. He has contributed to the 802.11n high throughput wireless LAN standard and has published extensively in leading international journals and conferences. He was awarded the CAL(IT)2 fellowship for pursuing graduate studies at the University of California San Diego and in 2009 he received the Upendra Patel Achievement Award for his efforts towards developing HSDPA/HSUPA/HSPA+ WCDMA technologies at Qualcomm. Since 2009 he has been a faculty member in the Electrical Engineering department at IIT Kanpur, where he is currently an Associate Professor, and is also associated with the BSNL-IITK Telecom Center of Excellence (BITCOE). At IIT Kanpur he has been awarded the P.K. Kelkar Young Faculty Research Fellowship (June 2012 to May 2015) for excellence in research. His popular video lectures for the NPTEL (National Programme on Technology Enhanced Learning) course on Advanced 3G and 4G Wireless Mobile Communications can found at the following YouTube link ( NPTEL 3G/4G - http://www.youtube.com/playlist?list=PLbMVogVj5nJSi8FUsvglRxLtN1TN9y4nx).
More info

Teaching Assistant(s)

Parul Srivastava

B.tech

IITK

Sarath Kumar

B.Tech

IITK

Saumya Dwivedi

PhD pursuing

IITK

Ankit Kudeshia

M.Tech

IITK

 Course Duration : Jul-Oct 2018

  View Course

 Enrollment : 18-Apr-2018 to 30-Jul-2018

 Exam registration : 25-Jun-2018 to 18-Sep-2018

 Exam Date : 28-Oct-2018

Enrolled

4057

Registered

157

Certificate Eligible

118

Certified Category Count

Gold

8

Silver

0

Elite

65

Successfully completed

45

Participation

8

Success

Elite

Gold





Legend

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score.
Applied Optimization for Wireless, Machine Learning, Big Data - Toppers list
Top 1 % of Certified Candidates

A CH MADHUSUDANARAO 100%

RAJIV GANDHI UNIVERSITY OF KNOWLEDGE TECHNOLOGIES


Top 2 % of Certified Candidates

SANDEEP BHAT 98%

WIRELESS RESEARCH LAB


Top 5 % of Certified Candidates

BHARATH S 97%

INDIAN INSTITUTE OF SCIENCE

ANUPAMA S 95%

IIT KANPUR

ADITYA SETHI 92%

INDIAN INSTITUTE OF TECHNOLOGY,BHUBANESWAR

SURENDRA KOTA 90%

INDIAN INSTITUTE OF TECHNOLOGY,KANPUR

MOHAMMAD AFROZ 90%

RAJIV GANDHI UNIVERSITY OF KNOWLEDGE TECHNOLOGIES

MONIKA JAIN 90%

THE LNMIIT

Enrollment Statistics

Total Enrollment: -1

Data Not Found..!
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Registration Statistics

Total Registration : 157

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.