【光华讲坛】Computational methods in applied econometrics

文:西南财经大学统计学院 发布时间:2018-09-26 浏览次数:108

 

主题:Computational methods in applied econometrics

主讲人:台湾清华大学  冼刍荛教授

时间:2018920日(星期四)下午2:00-3:30

 2018920日(星期四)下午4:00-5:30

      2018921日(星期五)上午10:00-11:30

地点:西南财经大学柳林校区通博楼B212会议室

主办单位:统计学院  科研处

主讲人简介:

Chor Yor Sin is a Professor in the Department of Economics at Tsing Hua University. She graduated with Ph.D. in Economics from California University of San Diego in 1993. His research interests include theoretical econometrics, applied time series econometrics, model selection.

主题一:Computational methods: Monte-Carlo Simulation and Re-sampling

主持人:统计学院陈坤副教授

时间:2018920日(星期四)下午2:00-3:30

内容摘要:

The advancement of computer technology makes computationally intensive methods possible. To name an example, while the Markov chain Monte Carlo method (MCMC) was first proposed in the 1950s, it was not be applicable till the 1990s. Apart from the MCMC method, this course also investigates other computationally intensive methods in the field of applied econometrics. While the MCMC method is now commonly used for Bayesian analysis, the Monte Carlo experiment is often found useful in checking the validity of asymptotic theories. Other computationally intensive methods will also be studied. In this course, the relationships between computational econometrics and some state-or-art machine learning tools such as support vector machine, support vector regression and deep learning will be discussed in detail. This course aims to equip the students with all these methods so that they are capable in applying them both in the academia and in the industry.

主题二:Computational methods: Classification and Support Vector Machine

主持人:统计学院陈坤副教授

时间:2018920日(星期四)下午4:00-5:30

内容摘要:

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.

In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance.

主题三:Computational methods: deep learning-introduction and feedforward networks

主持人:统计学院陈坤副教授

时间:2018921日(星期五)上午10:00-11:30

内容摘要:

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervisedsemi-supervised or unsupervised.

Deep learning architectures such as deep neural networksdeep belief networks and recurrent neural networks have been applied to fields including computer visionspeech recognitionnatural language processing, audio recognition, social network filtering, machine translationbioinformaticsdrug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brain), which make them incompatible with neuroscience evidences.

2
分享到: