My primary research interest involves the development of computational models of human language acquisition. Computational modeling is an effective tool for studying human cognition: whereas linguistic and psychological theories often give a high level explanation for the experimental data, computational models provide a detailed account of the underlying mechanisms for the cognitive task at hand. Moreover, the behaviour of a model can be directly compared to that of humans through computational simulation. One area of cognitive science that has extensively benefited from computational modeling is the study of natural language acquisition and use. However, developing computational algorithms that capture the complex structure of natural languages is an open problem. In particular, learning the abstract properties of language only from usage data without built-in knowledge of language structure remains a challenge. I am particularly interested in using appropriate machine learning techniques for the purpose of acquiring general knowledge of language from usage data. In my dissertation, I proposed a Bayesian, usage-based framework for modeling various aspects of early verb learning. The general constructions of language (such as transitive and intransitive) are viewed as a probability distribution over the syntactic and semantic features, e.g., the semantic properties of the verb and its arguments, and their relative word order in an utterance. Constructions are learned through clustering similar verb usages. Language use, on the other hand, is modeled as a Bayesian prediction problem, where the missing features in a usage are predicted based on the available parts and the acquired constructions (e.g., in sentence production, the best syntactic pattern for an utterance is predicted from the available semantic information). The model can successfully learn the common constructions of language, and its behaviour shows similarities to actual child data, both in sentence production and comprehension.