BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Karachi BEGIN:STANDARD DTSTART:20091031T230000 TZOFFSETFROM:+0600 TZOFFSETTO:+0500 TZNAME:PKT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210311T065513Z UID:55E91D9A-F853-4087-ABD2-746C7F1104FF DTSTART;TZID=Asia/Karachi:20201024T190000 DTEND;TZID=Asia/Karachi:20201024T200000 DESCRIPTION:Machine learning has become quite the trend in the fourth indus trial revolution and is not fizzling out any time soon. The robotics indus try is no stranger to applications of Machine Learning. So an event was co nducted to guide the freshers about how it really works. Demonstrated the working\, scenarios and the working principles of the applications of Mach ine Learning and AI. Uon Liaquat\, a very highly skilled expert in writing custom ML algorithms for learning robots so that they don’t need to be explicitly trained for each task. He is a student of UMT and is currently working on his new startup having theme Reinforcement learning in Robots. The main agenda of the topic of discussion was how to find a solution for a problem in ML and the steps involved for that purpose and to brief the s tudents how ML is really figured out and what they require to do for that purpose.\n\nUon Liaquat divided the informative and interactive session in to three major topics: Supervised Learning\, Unsupervised Learning\, and R einforcement Learning. Explained in detail through instances the phenomena \, and working of supervised\, unsupervised and reinforcement learning. Ur ged to make the session interactive for a better learning process. Explain ed different ways to reach a solution and develop multiple approaches to i mplement Machine Learning in Robotics.\n\nHe demonstrated how Supervised L earning is implemented in the problems where you already have a bunch of d ata and you know the result you want to implement.\n\nUnlikely\, unsupervi sed learning is used where you have a data set but you don’t know what t o implement.The speaker further explained that Reinforcement Learning (RL) is an area of machine learning concerned with how software agents ought t o take actions in an environment in order to maximize the notion of cumula tive reward. Reinforcement learning is one of three basic machine learning paradigms\, alongside supervised learning and unsupervised learning. Rese arch work on Reinforcement learning is still in process.\n\nThe speaker al so explained how to collect data sets from simple models and how are these utilized in machine learning. Algorithms should be coded using mathematic al techniques before developing or importing framework. Explained differen t approaches in manipulating data\, parallel processing and the embedding of algebraic and mathematical techniques in the backend.\n\nForbade to use frameworks in implementing the solution and start from scratch. Advised t o be goal-oriented in order to reach the core of Machine Learning. The spe aker conducted a Q&A session at the end of the session and cleared the que ries of attendees.\n\nVirtual: https://events.vtools.ieee.org/m/265006 LOCATION:Virtual: https://events.vtools.ieee.org/m/265006 ORGANIZER:sumamazaeem@gmail.com SEQUENCE:3 SUMMARY:Webinar on Embedded Machine Learning in Robotics URL;VALUE=URI:https://events.vtools.ieee.org/m/265006 X-ALT-DESC:Description: <br /><p><span style="font-weight: 400\;">Machine l earning has become quite the trend in the fourth industrial revolution and is not fizzling out any time soon. The robotics industry is no stranger t o applications of Machine Learning. So an event was conducted to guide the freshers about how it really works. Demonstrated the working\, scenarios and the working principles of the applications of Machine Learning and AI. Uon Liaquat\, a very highly skilled expert in writing custom ML algorithm s for learning robots so that they don&rsquo\;t need to be explicitly trai ned for each task. He is a student of UMT and is currently working on his new startup having theme Reinforcement learning in Robots.</span> <span st yle="font-weight: 400\;">The main agenda of the topic of discussion was ho w to find a solution for a problem in ML and the steps involved for that p urpose and to brief the students how ML is really figured out and what the y require to do for that purpose.</span></p>\n<p>&nbsp\;</p>\n<p><span sty le="font-weight: 400\;">Uon Liaquat divided the informative and interactiv e session into three major topics: Supervised Learning\, Unsupervised Lear ning\, and Reinforcement Learning. Explained in detail through instances t he phenomena\, and working of supervised\, unsupervised and reinforcement learning. Urged to make the session interactive for a better learning proc ess. Explained different ways to reach a solution and develop multiple app roaches to implement Machine Learning in Robotics.</span></p>\n<p>&nbsp\;< /p>\n<p><span style="font-weight: 400\;">He demonstrated how Supervised Le arning is implemented in the problems where you already have a bunch of da ta and you know the result you want to implement.</span></p>\n<p><span sty le="font-weight: 400\;">Unlikely\, unsupervised learning is used where you have a data set but you don&rsquo\;t know what to implement.</span><span style="font-weight: 400\;">The speaker further explained that Reinforcemen t Learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the n otion of cumulative reward. Reinforcement learning is one of three basic m achine learning paradigms\, alongside supervised learning and unsupervised learning. </span><span style="font-weight: 400\;">Research work on Reinfo rcement learning is still in process.</span></p>\n<p><span style="font-wei ght: 400\;">The speaker also explained how to collect data sets from simpl e models and how are these utilized in machine learning. Algorithms should be coded using mathematical techniques before developing or importing fra mework. Explained different approaches in manipulating data\, parallel pro cessing and the embedding of algebraic and mathematical techniques in the backend.</span><strong>&nbsp\;</strong></p>\n<p><span style="font-weight: 400\;">Forbade to use frameworks in implementing the solution and start fr om scratch. Advised to be goal-oriented in order to reach the core of Mach ine Learning.</span> <span style="font-weight: 400\;">The speaker conducte d a Q&amp\;A session at the end of the session and cleared the queries of attendees.</span></p> END:VEVENT END:VCALENDAR