Wed 26 August 2020 by Adrian Brasoveanu

See CV (pdf) for current information about teaching.

Note for prospective graduate students: It is not possible for me to respond to all emails from prospective students, although I would like to. If you are interested in working with me, please take a look at our department’s information about admissions https://linguistics.ucsc.edu/graduate/admissions.html, apply to our MA or PhD program in Linguistics through the regular channels, and feel free to mention my name in your application statement.

Older courses, with links to possibly useful materials:

Spring 2016:

  • Semantics B (Ling 232, UCSC), Graduate

Winter 2016:

  • Semantics II (Ling 116, UCSC), Undergraduate

Fall 2015:

  • Introduction to Linguistics (Ling 50, UCSC), Undergraduate

Summer 2015 (first session):

  • Semantics I (Ling 53, UCSC), Undergraduate

Spring 2015:

Winter 2015:

  • Semantics A (Ling 231, UCSC), Graduate
  • Quantitative Methods in Linguistics (Ling 147, UCSC), Undergraduate

Spring 2014:

  • Semantics B (Ling 232, UCSC), Graduate
    Sem B syllabus
  • Quantitative Methods in Linguistics (Ling 147, UCSC), Undergraduate
    Quantitative Methods syllabus

    Lecture notes and slides for Quantitative Methods:

    • quant_methods_lecture1.pdf: R as calculator, Variables, Vectors, Lists, More about string manipulation, The basics of working with (local) files
    • quant_methods_lecture2.pdf: Quick recap and some related issues, Patterned vectors, Logical operators, Indexing/slicing with logical operators, Set operators, Counting, Sorting, Printing / Saving to a file, Factors
    • quant_methods_lecture3.pdf: Basic graphics, Data frames, Saving a data frame to a file, Attaching and detaching data frames, Subsetting data frames, Ordering data frames, Lists, Character / String Processing, More graphics, The Brown Corpus
    • quant_methods_lecture4.pdf: Elementary Control (of) Flow Structures (IF, FOR, Example: obtaining the first 12 Fibonacci numbers, WHILE), Taking advantage of the vectorial nature of R and 3 example problems, General programming tips (Top-down design, Debugging and maintenance, Measuring the time your program takes, i.e., very basic profiling), Goals of quantitative modeling and analysis and types of data, Introducing the idea of a probability distribution: Frequency distributions, Becoming familiar with the normal distribution
    • quant_methods_lecture4_related_slides1_intro_prob1.pdf: Sample Spaces and Events (Sample Spaces, Events, Axioms and Rules of Probability), Joint, Conditional and Marginal Probability, Bayes’ Theorem, Independence and Conditional Independence, Random Variables and Probability Distributions, Expectation
    • quant_methods_lecture4_related_slides2_intro_prob2.pdf: Probability: Frequency vs Reasonable Expectation, Generalizing Classical Logic, Patterns of Plausible Inference (Modus Ponens, Modus Tollens, Affirming the Consequent, Denying the Antecedent, Affirming the Consequent of Weaker/Plausible Implications, Probability Theory as the Logic of Data Analysis
    • quant_methods_lecture5.pdf: Introducing least squares models — Data generation, First attempt: the mean of y, Quantifying the error, From sample to population: basic statistical (inductive) inference (Sample size, Sample variance, Putting the two together: the standard error (SE) of the mean, the Central Limit Theorem (CLT), and 95% confidence intervals (CIs)), Understanding the lm output a little bit better: t-distributions, Applying the Central Limit Theorem (CLT) to Bernoulli distributed data (The Bernoulli distribution, The binomial distribution, The binomial distribution and the Central Limit Theorem)
    • quant_methods_lecture6.pdf: Second attempt: two means, i.e., predicting y values based on x2 values, The two-mean linear (regression) model, Analysis of Variance (ANOVA) tables, T-tests: the simple version of the F-test we can use for single categorical parameters, Third attempt: ‘many’ means, i.e., predicting y values based on x1 values, Comparing reg1 with the 1-mean model, The intercept and slope coefficients (Alternative slopes, Alternative intercepts, Alternative intercepts and slopes)
    • quant_methods_lecture7.pdf: Recap and related issues (scatter plot matrices), R-squared, Correlation, An alternative way of calculating correlation, Inference for simple linear regression: from sample to population, The sampling error for the slope, The sampling error for the intercept, Putting it all together: plotting predictions for linear regression models
    • quant_methods_lecture8.pdf: Recap, Fourth attempt: multiple linear regression, Graphical comparison of reg1, reg2 and reg3, ANOVA and model selection, Adding interactions, Interpreting interactions, More on interactions (Multicollinearity and variable centering, Another example of regression with interaction terms)
    • quant_methods_lecture9.pdf: Essentials of linear models (The stochastic component of linear models: probability distributions, The deterministic component of linear models: linear predictors and design matrices), T-tests: more realistic examples (T-test with equal variances, T-test with unequal variances), Simple linear regression (Interpretation of confidence intervals), One-way ANOVA, Random-effects ANOVA, Two-way ANOVA (Aside: using simulation to assess the bias and precision of an estimator, Analysis of two-way ANOVA data), Linear mixed-effects models (random-intercepts models, random-coefficients model without correlation between intercepts and slopes, random-coefficients model with correlation between intercepts and slopes)
    • quant_methods_lecture10.pdf: Basic introduction to logistic regression, Generalized linear models (GLMs) (The identity link, The log link, The logit link), More about odds and log-odds, i.e., logits, The standard logistic distribution, The logistic regression for the CHD~AGE data, A couple of simple examples of GLMs (4 models and their visualization), Model comparison (Deviance and log-likelihood ratios, Background on likelihood functions and maximum likelihood estimates (MLEs), Evaluating the interaction model, Adding polynomial functions as additional predictors)
    • quant_methods_lecture11.pdf: Another logistic regression example: manipulating reasonable doubt, Simulating datasets for logistic regression, A linguistic example: /s/-deletion in Panamanian Spanish, Long format data & logistic regression

    The homework assignments, which were language/linguistics-centric, are available upon request.

Winter 2014:

  • Semantics I (Ling 53, UCSC), Undergraduate

Fall 2013:

Winter 2013:

  • Semantics II (Ling 116, UCSC), Undergraduate
  • Research Seminar (Ling 290, UCSC), Graduate

Fall 2012:

  • Semantics I (Ling 53, UCSC), Undergraduate
  • Seminar: Introduction to Bayesian Data Analysis & Cognitive Modeling (Ling 239, UCSC), Graduate; see the syllabus for a description of the goals of the course and a tentative schedule

Summer 2012:

  • Semantics I (Ling 53, UCSC), Undergraduate

Spring 2012:

  • Structure of Romance Languages (Ling 181, UCSC), Undergraduate

Winter 2012:

  • Semantics B (Ling 232, UCSC), Graduate
  • Seminar in Semantics: Statistical & Cognitive Modeling for Formal Semantics (Ling 239, UCSC), Graduate

    Description of the broader research program:

    Capturing the particular ways in which natural language interpretation proceeds is usually taken to involve rich abstract representations and fairly complex operations over such representations. Under this view, two general goals of formal semantics are to (i) identify patterns of interpretation that seem to involve such abstract (non-overt / latent) representations and operations and (ii) design logical systems in which the ‘right’ range of representations and operators can be defined and in which these representations and operators interact in the ‘right’ way.
    At the same time, providing solid empirical foundations for increasingly sophisticated formal semantic theories requires increasingly sophisticated methods of empirical investigation and statistical analysis of the resulting data.
    In addition, semantic theories should be complemented and further constrained by cognitive theories of (i) how such structured, abstract and compositionally assembled representations and operations can be learned / induced from ‘raw’ observed data and (ii) the kinds of mechanisms that underlie the processing of such representations and operations in actual natural language usage.
    The seminar will focus on establishing and solidifying multiple connections between detailed, formally sophisticated semantic theories (of quantifier scope, interpretation of indefinites etc.) and modern Bayesian methods of data analysis, as well as cognitive models (based on Bayesian ideas) of learning abstract, highly structured representations of the kind deployed in formal semantics.

    Schedule (subject to change):

    • Intro to Statistical & Cognitive Modeling (Week 1)
    • Intro to Bayesian Data Analysis (Weeks 1-4)
    • Quantifier Scope, Indefinites and Sentence-internal Readings (Weeks 4-8)
    • Bayesian Cognitive Modeling for Formal Semantics: Learning the Meaning of Number Words and Quantifiers (Weeks 8-10) * Course materials:

    • readings available on the eCommons page for the course

    • various R scripts, handouts etc. will be posted on the LaLoCo page

Summer 2011:

  • Semantics I (Ling 53, UCSC), Undergraduate

Winter 2011:

  • Semantics I (Ling 53, UCSC), Undergraduate
  • Semantics B (Ling 232, UCSC), Graduate

Spring 2010:

Winter 2010:

  • Semantics I (Ling 53, UCSC), Undergraduate

Spring 2009:

  • Semantics II (Ling 116, UCSC), Undergraduate
  • Senior Research Seminar (Ling 190, UCSC), Undergraduate

Winter 2009:

  • Semantics B (Ling 232, UCSC), Graduate
    Time: MW 2-3:45 ++ Location: The Cave
    Office hours: TBA & by email appointment
    Description: The goal of the course is to give the participants the technical skills to understand the Montagovian solution to the problem of compositionality - that is, to understand how the meaning of a natural language expression is a function of the meanings of its subexpressions and the way they are syntactically put together. To put it differently, we will learn the basics of rigorously designing a syntax-semantics interface in the Montagovian tradition. Our specific goal is to be able to read Montague’s PTQ and Hendriks 1993 Ch.1 by the end of the quarter. Our textbook is Dowty et al 1981 Introduction to Montague Semantics. The readings for the course are available on the WebCT page for the course, which you can access by logging into WebCT and selecting this link: LING-232 Semantics B, #37056.

Fall 2008:

Spring 2008:

Fall 2007:

  • Seminar in Semantics: Indefinites (Ling 237, Stanford), Graduate
    Time: TBA ++ Location: TBA
    Office hours: TBA & by email appointment
    Description: Static and dynamic approaches to the semantics of indefinites (and definites). Their referential vs. quantificational status, their scopal properties (exceptional wide scope), their interaction with modal anaphora & quantification. Indefinites cross-linguistically, types of indefinites (argumental vs. predicative, bare nouns, NPI’s, free choice, wh-indefinites, specific indefinites etc.) and their semantic / pragmatic properties. Indefinite-like items in the modal, temporal / aspectual & degree domains.

Spring 2007:

Winter 2007:

  • Introduction to Linguistics (Ling 20, UCSC), Undergraduate
    Time: MWF 2-3:10 ++ Location: Baskin Engineering 152
    Office hours: TBA & by email appointment

Fall 2006:

  • Introduction to Dynamic Semantics, Graduate, cotaught with Sam Cumming ++ Invited mini-course, Dynamic Semantics Workshop, Institutt for filosofi, ide-og kunsthistorie og klassiske (IFIKK), University of Oslo
    Here are the syllabus, some of the slides, and the corresponding handout.
  • Introduction to the Study of Language (Linguistics 101, Rutgers University), Undergraduate
    Here are the handouts for the presupposition and conversational implicature (part 1 & part 2) lectures.