As machine learning (ML) becomes integral in more disciplines, introductory courses in the field are attracting increasingly diverse audiences. Design of these introductory ML courses needs to be theoretically sound, but also intuitive, engaging, and accessible to a range of students. Effective teaching of ML must go beyond teaching the theoretical or practical mechanics of algorithms. In this paper, we synthesize effective teaching strategies from 6 experienced ML instructors across 5 institutions to help students define appropriate ML problems, build intuition, develop reasoning skills, and apply models responsibly. We organize these strategies into eight thematic areas: preparing students for success, motivating learners through real-world relevance, integrating ethics and societal impact, avoiding common methodological pitfalls in model evaluation, guiding students on design decisions, adapting effective classroom practices, assessing student learning, and preparing for the future. Each section offers practical examples of classroom-tested activities (or references to existing resources), and in many cases, reflections on our experiences with the strategies. Our aim is for this paper to be a starting point for instructors aiming to improve learning in introductory ML courses. We hope this is a resource-rich guide for teaching ML to diverse learners, grounded in both pedagogy and practice.

This article provides an extensive overview of the literature related to the phenomenon of non-nominal-antecedent anaphora (also known as abstract anaphora or discourse deixis), a type of anaphora in which an anaphor like “that” refers to an antecedent (marked in boldface) that is syntactically non-nominal, such as the first sentence in “It’s way too hot here. That’s why I’m moving to Alaska.” Annotating and automatically resolving these cases of anaphora is interesting in its own right because of the complexities involved in identifying non-nominal antecedents, which typically represent abstract objects such as events, facts, and propositions. There is also practical value in the resolution of non-nominal-antecedent anaphora, as this would help computational systems in machine translation, summarization, and question answering, as well as, conceivably, any other task dependent on some measure of text understanding. Most of the existing approaches to anaphora annotation and resolution focus on nominal-antecedent anaphora, classifying many of the cases where the antecedents are syntactically non-nominal as non-anaphoric. There has been some work done on this topic, but it remains scattered and difficult to collect and assess. With this article, we hope to bring together and synthesize work done in disparate contexts up to now in order to identify fundamental problems and draw conclusions from an overarching perspective. Having a good picture of the current state of the art in this field can help researchers direct their efforts to where they are most necessary. Because of the great variety of theoretical approaches that have been brought to bear on the problem, there is an equally diverse array of terminologies that are used to describe it, so we will provide an overview and discussion of these terminologies. We also describe the linguistic properties of non-nominal-antecedent anaphora, examine previous annotation efforts that have addressed this topic, and present the computational approaches that aim at resolving non-nominal-antecedent anaphora automatically. We close with a review of the remaining open questions in this area and some of our recommendations for future research.

OBJECTIVES: It has become regular practice to de-identify unstructured medical text for use in research using automatic methods, the goal of which is to remove patient identifying information to minimize re-identification risk. The metrics commonly used to determine if these systems are performing well do not accurately reflect the risk of a patient being re-identified. We therefore developed a framework for measuring the risk of re-identification associated with textual data releases. METHODS: We apply the proposed evaluation framework to a data set from the University of Michigan Medical School. Our risk assessment results are then compared with those that would be obtained using a typical contemporary micro-average evaluation of recall in order to illustrate the difference between the proposed evaluation framework and the current baseline method. RESULTS: We demonstrate how this framework compares against common measures of the re-identification risk associated with an automated text de-identification process. For the probability of re-identification using our evaluation framework we obtained a mean value for direct identifiers of 0.0074 and a mean value for quasi-identifiers of 0.0022. The 95% confidence interval for these estimates were below the relevant thresholds. The threshold for direct identifier risk was based on previously used approaches in the literature. The threshold for quasi-identifiers was determined based on the context of the data release following commonly used de-identification criteria for structured data. DISCUSSION: Our framework attempts to correct for poorly distributed evaluation corpora, accounts for the data release context, and avoids the often optimistic assumptions that are made using the more traditional evaluation approach. It therefore provides a more realistic estimate of the true probability of re-identification. CONCLUSIONS: This framework should be used as a basis for computing re-identification risk in order to more realistically evaluate future text de-identification tools.

Shell nouns are abstract nouns, such as fact, issue, idea, and problem, which, among other functions, facilitate efficiency by avoiding repetition of long stretches of text.An example is shown in (1).Shell nouns encapsulate propositional content, and the process of identifying this content is referred to as shell noun resolution.(1) Living expenses are much lower in rural India than in New York, but this fact is not fully captured if prices are converted with currency exchange rates.This dissertation presents the first computational work on resolving shell nouns.The research is guided by three primary questions: first, how an automated process can determine the interpretation of shell nouns; second, the extent to which knowledge derived from the linguistics literature can help in this process; and third, the extent to which speakers of English are able to interpret shell nouns.I start with a pilot study to annotate and resolve occurrences of this issue in the Medline abstracts.The method follows a typical problem-solving procedure used in computational linguistics: manual annotation, feature extraction, and supervised machine learning.The results illustrate the feasibility of annotating and resolving shell nouns, at least in the closed domain of Medline abstracts.Next, I move to developing general algorithms to resolve a variety of shell nouns in the newswire domain.The primary challenge was that each shell noun has its own idiosyncrasies and there was no annotated data available for this task.I developed a number of computational methods for resolving shell nouns that do not rely on manually annotated data.

Interpreting anaphoric shell nouns (ASNs) such as this issue and this fact is essential to understanding virtually any substantial natural language text.One obstacle in developing methods for automatically interpreting ASNs is the lack of annotated data.We tackle this challenge by exploiting cataphoric shell nouns (CSNs) whose construction makes them particularly easy to interpret (e.g., the fact that X).We propose an approach that uses automatically extracted antecedents of CSNs as training data to interpret ASNs.We achieve precisions in the range of 0.35 (baseline = 0.21) to 0.72 (baseline = 0.44), depending upon the shell noun.

We develop an acoustic feature set for the estimation of a per-son’s age from a recorded speech signal. The baseline features are Mel-frequency cepstral coefficients (MFCCs) which are ex-tended by various prosodic features, pitch and formant frequen-cies. From experiments on the University of Florida Vocal Ag-ing Database we can draw different conclusions. On the one hand, adding prosodic, pitch and formant features to the MFCC baseline leads to relative reductions of the mean absolute error between 4-20%. Improvements are even larger when percep-tual age labels are taken as a reference. On the other hand, reasonable results with a mean absolute error in age estimation of about 12 years are already achieved using a simple gender-independent setup and MFCCs only. Future experiments will evaluate the robustness of the prosodic features against channel variability on other databases and investigate the differences be-tween perceptual and chronological age labels.