Casting a Wider (Neural) Net: Introducing Data Science and Machine Learning to a Larger Audience
A presentation introducing foundational concepts related to Data Science and Machine learning was given five times over a one year period to audiences with different backgrounds. This report summarizes the successes and failures in delivering this material. Insights about the topic and useful teaching resources are presented.
Machine Learning for Business Students: An Experiential Learning Approach
This paper reports on the design and teaching of a course on Machine Learning (ML) for business students, focusing on the interaction between content and teaching approach. We demonstrate that the nature of ML technology is amenable to experiential learning and is well suited for business students as users of the technology. The community-based tools, documentation and datasets enable non-programmers to use and adapt open, public-domain ML examples. Students learn how to select algorithms for specific data and tasks, experiment with hyper-parameters and neural-network structures, evaluate results and interpret their business implications. We conclude that business students, at least in our school, often have good abstract understanding of computing and may be ready for deeper learning of digital technology. The general-purpose nature of ML makes it suitable for real-world business problems, making it business-relevant technology that should be introduced into business schools.
Kinematic Modelling and Simulation of a 2R Robot Using SolidWorks and Verification by MATLAB / Simulink Regular Paper
The  simulation  of  robot  systems  is  becoming very popular, especially with  the  lowering of  the cost of computers,  and  it  can  be  used  for  layout  evaluation, feasibility studies, presentations with animation and off‐ line programming. The  trajectory  planning  of  redundant manipulators  is  a very  active  area  since  many  tasks  require  special characteristics  to  be  satisfied.  The  importance  of redundant manipulators has  increased over  the  last  two decades  because  of  the  possibility  of  avoiding singularities  as  well  as  obstacles  within  the  course  of motion.  The  angle  that  the  last  link  of  a  2  DOF manipulator makes with the x‐axis is required in order to find the solution for the inverse kinematics problem. This angle  could  be  optimized  with  respect  to  a  given specified  key  factor  (time,  velocity,  torques)  while  the end‐effector  performs  a  chosen  trajectory  (i.e., avoiding an obstacle) in the task space. Modeling  and  simulation  of  robots  could  be  achieved using  either  of  the  following  models:  the  geometrical model (positions, postures), the kinematic model and the dynamic model. To  do  so,  the  modelization  of  a  2‐R  robot  type  is implemented. Our main  tasks  are  comparing  two  robot postures with the same trajectory (path) and for the same length  of  time,  and  establishing  a  computing  code  to obtain the kinematic and dynamic parameters. SolidWorks  and MATLAB/Simulink  softwares  are  used to check the theory and the robot motion simulation. This  could  be  easily  generalized  to  a  3‐R  robot  and possibly therefore to any serial robot (Scara, Puma, etc.). The verification of the obtained results by both softwares allows  us  to  qualitatively  evaluate  and  underline  the validityof  the  chosen  model  and  obtain  the  right conclusions. The results of  the simulations are discussed and an agreement between the two softwares is certainly obtained.