One of the main aspirations of AI is to design intelligent systems that can communicate with people naturally and effectively. Designing such systems poses challenges that encompass multiple academic disciplines. In this course, we will discuss recent advances that bring together tools and theories from natural language processing, computer vision, robotics, linguistics, and spatial and social cognition to address these challenges.
In the first half of the class, we will discuss computational models of discourse with real-world applications, machine learning approaches for analyzing and modeling multimodal web data, and crowdsourcing for creating robust and interpretable systems. In the second half, we will study how we can extend these models and practices to model language generation, reasoning, and cooperation in interactive systems. Finally, we will discuss open problems in conversational AI, common sense inference and human-robot collaboration.
Statistics Fall 2014
Class size: ~150
This course covers descriptive and inferential statistics. The descriptive portion analyzes data through graphs, measures of central tendency and dispersion. The inferential statistics portion covers statistical rules to compute the basic probability, including binomial, normal, Chi-squares, and T-distributions. This course also covers the estimation of population parameters, hypothesis testing, linear regression, correlation, and ANOVA. Emphasis is placed on applications of technology, using software packages, for statistical analysis and interpretation of statistical values based on data from disciplines including business, social sciences, psychology, life science, health science, and education. This course is intended for transfer students interested in statistical analysis.
Statistics and Probability for MBA
Class size: ~100
This course provides the fundamental methods of statistical analysis, art and science of extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory, and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes.