Browsing by Subject "Active Learning"
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Item Open Access Comparing Active Learning to Team-Based Learning in Undergraduate Neuroscience.(Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience, 2020-01) Ng, Minna; Newpher, ThomasTeam-based learning (TBL) is a special form of collaborative learning that involves the use of permanent working teams throughout the semester. In this highly structured and interactive teaching method, students perform preparatory activities outside of class to gain factual knowledge and understand basic concepts. In class, students collaborate with peers to apply content, analyze findings, and synthesize new ideas. To better understand the learning outcomes specific to TBL courses, we analyzed end-of-semester course evaluations from an undergraduate neuroscience course taught using either a moderate structure active learning or TBL format. Our analysis reveals that the TBL taught classes had significantly higher levels of self-reported learning in the areas of gaining, understanding, and synthesizing knowledge. We propose that these gains are driven by the TBL readiness assurance process and peer evaluations. Both of these structural components are expected to increase student accountability, motivation, and engagement with course content.Item Open Access Data-Driven Learning Models with Applications to Retail Operations(2018) Modaresi, SajadData-driven approaches to decision-making under uncertainty is at the center of many operational problems. These are problems in which there is an element of uncertainty (e.g., customer demand) that needs to be estimated (learned) from data (e.g., customer transaction data) in order to make online (dynamic) operational (e.g., assortment) decisions. This dissertation adopts a data-driven active learning approach to study various operational problems under uncertainty with a focus on retail operations.
The first two essays in this dissertation study the classic exploration (i.e., parameter estimation) versus exploitation (i.e., optimization) trade-off from different perspectives. The first essay takes a theoretical approach and studies such trade-off in a combinatorial optimization setting. We show that resolving the exploration versus exploitation trade-off efficiently is related to solving a Lower Bound Problem (LBP), which simultaneously answers the questions of what to explore and how to do so. We establish a fundamental limit on the asymptotic performance of any admissible policy that is proportional to the optimal objective value of the LBP problem. We also propose near-optimal policies that are implementable in real-time. We test the performance of the proposed policies through extensive numerical experiments and show that they significantly outperform the relevant benchmark.
The second essay considers the dynamic assortment personalization problem of an online retailer facing heterogeneous customers with unknown product preferences. We propose a prescriptive approach, called the dynamic clustering policy, for dynamic estimation of customer preferences and optimization of personalized assortments. We test the proposed approach with a case study based on a dataset from a large Chilean retailer. The case study suggests that the benefits of the dynamic clustering policy under the Multinomial Logit (MNL) model can be substantial and result (on average) in more than 37% additional transactions compared to a data-intensive policy that treats customers independently and in more than 27% additional transactions compared to a linear-utility policy that assumes that product mean utilities are linear functions of available customer attributes.
Further focusing on retail operations, the final essay studies the interplay between a retailer's return and pricing policies and customers' purchasing decisions. We characterize the retailer's optimal prices in the cases with and without product returns and determine conditions under which offering the return option to customers increases the retailer's revenue. We also explore the impact of accounting for product returns on demand estimation. The preliminary numerical results based on a real dataset suggest that our model, which accounts for product returns, increases demand estimation accuracy compared to models that do not consider product returns in their estimation.
Item Open Access Deep Learning for the modeling and design of artificial electromagnetic materials(2023) Ren, SimiaoArtificial electromagnetic materials (AEMs) are materials that exhibit unusual electromagnetic properties. With sub-wavelength, periodic structures, AEMs can achieve incredible abilities to manipulate light, like the cloaking effect of the “invisibility cloak” in the Harry Potter movie. Apart from the cinematic application of invisibility, AEMs have important applications ranging from high-efficiency solar panels to next-generation communications systems. The major goal of this thesis is to develop deep learning tools to design materials that have increasingly customized interactions with electromagnetic waves, thus enabling more useful technologies. In turn, this necessitates the modeling and design of increasingly complicated materials. Modeling of these materials is difficult because (i) the physics of advanced materials is intrinsically more complicated with no simple analytical form, (ii) the manufacture of such nano-structures is prohibitively expensive, and (iii) the computational electromagnetic simulation software is too slow to iterative through trail-n-error. Recently, the advancement of deep learning bring new perspectives on such a problem. In this thesis, we explore deep learning for the modeling and design of advanced photonic materials. In particular, we explore and make important contributions to two fundamental areas: inverse design, and active learning. In inverse design, we develop an accurate method, “Neural-adjoint,” and show its dominance not only in simple inverse problems but also in contemporary AEM design problems. We further analyze and benchmark eight state-of-the-art deep inverse approaches in the AEM inverse design and discover that the one-to-manyness of the problem is an important factor in such a problem. Then, motivated by the immediate drawback that all deep inverse models require a large set of labeled data, we investigate the benefit of active learning in the setting of AEM design and scientific computing in general. By setting the problem close to a real application where pool size is unknown, we find the majority of deep regression pool-based active learning methods in our benchmark lack robustness and don’t outperform even random sampling consistently.
Item Open Access Nonlinear Energy Harvesting With Tools From Machine Learning(2020) Wang, XuesheEnergy harvesting is a process where self-powered electronic devices scavenge ambient energy and convert it to electrical power. Traditional linear energy harvesters which operate based on linear resonance work well only when excitation frequency is close to its natural frequency. While various control methods applied to an energy harvester realize resonant frequency tuning, they are either energy-consuming or exhibit low efficiency when operating under multi-frequency excitations. In order to overcome these limitations in a linear energy harvester, researchers recently suggested using "nonlinearity" for broad-band frequency response.
Based on existing investigations of nonlinear energy harvesting, this dissertation introduced a novel type of energy harvester designs for space efficiency and intentional nonlinearity: translational-to-rotational conversion. Two dynamical systems were presented: 1) vertically forced rocking elliptical disks, and 2) non-contact magnetic transmission. Both systems realize the translational-to-rotational conversion and exhibit nonlinear behaviors which are beneficial to broad-band energy harvesting.
This dissertation also explores novel methods to overcome the limitation of nonlinear energy harvesting -- the presence of coexisting attractors. A control method was proposed to render a nonlinear harvesting system operating on the desired attractor. This method is based on reinforcement learning and proved to work with various control constraints and optimized energy consumption.
Apart from investigations of energy harvesting, several techniques were presented to improve the efficiency for analyzing generic linear/nonlinear dynamical systems: 1) an analytical method for stroboscopically sampling general periodic functions with arbitrary frequency sweep rates, and 2) a model-free sampling method for estimating basins of attraction using hybrid active learning.