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: None
CORE/ELECTIVE: Elective
UG/PG: PG
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
38479 students have enrolled already!!
ABOUT THE INSTRUCTOR:
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 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
SUGGESTED READING MATERIALS:
Own course notes, copy of ppts. Machine Learning textbooks as optional background material.
CERTIFICATION EXAM :
The exam is optional for a fee.
Date and Time of Exams: April 28 2019 (Sunday) 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.
CERTIFICATION:
Final score will be calculated as : 25% assignment score + 75% final exam score
25% assignment score will be calculated as follows:
Assignments A:
Best 5 assignments out of 7 will be considered with a weightage of 15%
Assignments B:
Best 5 assignments out of 6 will be considered with a weightage of 10%
E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IITMadras.It will be e-verifiable at nptel.ac.in/noc.
