Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

IMPLEMENTATION OF DOUGH MOULDING MACHINE AS A LEARNING MEDIA TO IMPROVE MACHINE ELEMENTS LEARNING OUTCOMES

Using a dough moulding machine as a learning tool, improving student learning outcomes in the Machine Elements course, and planning the transmission of belts and pulleys for dough moulding machines were the three goals of this study. Classroom Action Research (CAR) was the method employed in this study. According to the results, the dough moulding machine was implemented as a teaching tool in the Machine Elements course over the course of two cycles, with each cycle consisting of the planning, action, observing, and reflecting phases. In the study's conclusion, it is stated that the use of teaching aids in the Machine Elements course can enhance student learning outcomes. An increase from cycle I to cycle II was indicative of improved learning outcomes. The percentage of student learning outcomes that are completed has increased, with the first cycle's completion rate reaching 56,25% of students and the second cycle's completion rate reaching 100 percent of the 16 students who achieve KKM > 60. Each cycle is characterised by an increase in the assessment of student attitudes and skills

Helicopters as a Theme in a Machine Design Course

Helicopters as a Theme in Teaching Machine Design A machine design course is required in most undergraduate mechanical engineering curricula.This course generally covers an introduction to mechanical engineering design, a review of materials engineering, a review of mechanics of materials (shear force and bending moment diagrams, stress and strain analysis, deflection and stiffness analysis of beams, columns, etc.),models for failure due to static loading and variable fatigue, and then presents (in somewhat arbitrary order) the design of specific mechanical elements: shafts, fasteners, springs, bearings,gears, flexible elements such as belts, chain, and wire rope, clutches, brakes, couplings, etc.For some topics in machine design it is not possible to develop analytical models from first principles, as is done in fluid mechanics or thermodynamics. Rather, there are guidelines and rules of thumb and equations that include factors that must be taken on blind faith and somehow used to get an approximate answer. The approach can be unsatisfying, arbitrary, and not meaningful unless it is tied to real-world problems.To help motivate student learning, foster interest in the topics, and make the material more alive,we are testing the idea of studying helicopters and their components throughout the course as a theme to teach students about the different mechanical elements. Helicopters are an ideal system to exemplify the concepts taught in the course because all aspects of machine design are encapsulated in the design of a helicopter and the price of failure of the components or design is high (human fatality). In the standard helicopter configuration, two turbine jet engines are used to drive a main rotor and a tail rotor and the pilot controls are mechanically linked to both rotors to allow for handling of the aircraft.For each topic in the course the connection to helicopters is presented and helicopter design challenges are posed. For example, the shafts and gearboxes used to transfer energy from the high-speed turbine engines to the low speed rotors can be used to teach students about shaft bending, gear design, and fatigue failure. When asked to design a gearbox to achieve the speed reduction between the turbine jet engine and the main rotor, students discover why planetary gears are used. Other topics such as clutches, brakes, couplings, fasteners, springs, and vibration effects are all prominent features of helicopter design. They serve as excellent motivating examples to show students the real-life applications of machine design concepts.In closing, students generally view the machine design course as very challenging and, due to themany specific machine elements covered, have difficulty seeing how the separate components fit within the needs for a real system. To address this concern, enhance learning, and bring more excitement to the topics, we explored the value of using a theme physical system, namely helicopters and their components, to bring the material to life when teaching machine design

When to Use Machine Learning: A Course Assignment

The number of institutions that offer machine learning courses continues to increase. However, supplementary materials that help instructors teach these courses fail to address an important step in the machine learning process; that is, conceptualizing a problem using a valid input-output relationship. To address this issue, I first review frameworks in extant work before proposing a decision flow. After discussing steps in the decision flow, I present a course assignment that reinforces the concepts in the decision flow. I conclude by discussing the lessons learned after using this assignment in a graduate course at a university in the United States

Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines

In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural network) with uniformly distributed weights as our model of study, we numerically measure how complexity changes as a function of network dynamics and network parameters. We apply an extension of one such information-theoretic measure of complexity to understand incremental Hebbian learning in Hopfield networks, a fully recurrent architecture model of autoassociative memory. In the course of Hebbian learning, the total information flow reflects a natural upward trend in complexity as the network attempts to learn more and more patterns.Comment: 16 pages, 7 figures; Appears in Entropy, Special Issue "Information Geometry II

Simon Rogers and Mark Girolami: A first course in machine learning

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch

Educational Resources for Self-learning of Descriptive Geometry

Proceeding of: 2nd International Symposium on the Education and Mechanism and Machine Science (ISEMMS 2017), 23-24 November 2017, Leganés, Madrid.In this work, two educational resources for self-learning of Descriptive Geometry are presented: the “Zero Course” and the “Support Course”. The creation of this e-learning material responds to the need that our students at University Carlos III de Madrid have to reach a minimum level at the beginning (Zero Course), and during (Support Course) the first course on technical drawing. First, the need is made out through results of surveys carried out in previous years. Then, some e-learning applications are exposed, among which the most appropriate to the need are chosen. Finally, the designed courses are described including all the technical resources. The results of the surveys carried out on the students of these courses, as well as some statistics of their qualifications, are also presented.Publicad

Convex Optimization for Machine Learning

This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning. A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python

An Adaptable and Transferrable Project Based on a Heart-lung Machine Design Challenge

A heart-lung machine is used to take over the function of the heart and lungs during a surgical procedure in which the heart must be stopped. This machine makes possible a variety of lifesaving surgeries such as heart transplants, bypass surgery, and valve replacement. Blood oxygenators are used in more than one million procedures annually, and their total market is over $500 million per year. This paper describes how a heart-lung machine design challenge was used in four different educational contexts: high school science courses in the United States, a multidisciplinary first year engineering course at a university in the United States, a second year chemical & bioprocess engineering course at a university in Ireland, and an upper level chemical engineering core course (Transport II). The design challenge required students to design, build, and test a heart-lung machine to simulate the performance of a clinical cardiopulmonary bypass system. The project proved to be adaptable and transferrable to different contexts with different learning objectives, assessment, instructional strategy, student population, and details of implementation