Machine Learning,ML

By Prof. Carl Gustaf Jansson   |   KTH, The Royal Institute of Technology
Learners enrolled: 23990
The scientific discipline of Machine Learning focuses on developing algorithms to find patterns or make predictions from empirical data. It is a classical sub-discipline within Artificial Intelligence (AI). The discipline is increasingly used by many professions and industries to optimize processes and implement adaptive systems. The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree based inductive learning, inductive logic programming, reinforcement learning and deep learning through decision trees.

INTENDED AUDIENCE  : Interested students
PREREQUISITES : Relevant applied math and statistics, core computer sciencel
INDUSTRY SUPPORT : Broad industrial interest at present, i.e. for autonomous vehicles, robots, intelligent assistants and general datamining
Course Status : Completed
Course Type : Elective
Duration : 8 weeks
Category :
  • Computer Science and Engineering
Credit Points : 2
Level : Postgraduate
Start Date : 24 Feb 2020
End Date : 17 Apr 2020
Enrollment Ends : 24 Feb 2020
Exam Date : 26 Apr 2020 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1  :  Introduction to the Machine Learning course
Week 2  :  Characterization of Learning Problems
Week 3  :  Forms of Representation
Week 4  :  Inductive Learning based on Symbolic Representations and Weak Theories
Week 5  :  Learning enabled by Prior Theories
Week 6  :  Machine Learning based  Artificial Neural Networks
Week 7  :  Tools and Resources + Cognitive Science influences
Week 8  :  Examples, demos and exam preparations

Books and references

Own course notes, copy of ppts. Machine Learning textbooks as optional background material. 

Instructor bio

Prof. Carl Gustaf Jansson

KTH, The Royal Institute of Technology
Carl Gustaf Jansson is tenured Professor in Artificial Intelligence at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. His research contributions are mostly in artificial intelligence, in particular Knowledge Representation and Machine Learning. Particular research interests are intelligent interfaces and ubiquitous computing.Henrik Boström is tenured professor in computer science and data science at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm. His research focuses on machine learning algorithms and applications, in particular ensemble learning and interpretable models, including decision trees and rules, and conformal predictio. He is also a senior researcher at the Swedish institute RISE SICS.Fredrik Kilander is Associate Professor in Computer Science at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm. His PhD was in Machine Learning in particular Conceptual Clustering. A particular research interest is ubiquitous computing. Dr Kilander has a broad experience from teaching in Computer Science in particular Programming Methodology.

Course certificate

• The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
• The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 26th April 2020, Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
• Registration url: Announcements will be made when the registration form is open for registrations.
• The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
• Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

• Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course. 
• Exam score = 75% of the proctored certification exam score out of 100
• Final score = Average assignment score + Exam score

• If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
• Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras. It will be e-verifiable at nptel.ac.in/noc.
• Only the e-certificate will be made available. Hard copies will not be dispatched.

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