Teaching

If you are in the Tokyo area, I hope you will consider taking my classes! If you are not at the University of Tokyo or if you are not in academia, you will not be able to receive any credit, but you are still welcome to audit the classes. In this case I would appreciate if you could email me in advance so that I know how many class participants to expect. (Note: It's not me in the picture.)

This year, just like last year, I teach three courses: Data Science for Practical Economic Research - from April 2018, Deep Learning and Related Methods for Large Dataset Information Processing - from September 2018; Topics in Asset Pricing - from September 2018. The details are below. My other teaching includes International Finance and International Trade.

Deep Learning and Related Methods for Large Dataset Information Processing


Schedule: Mondays 10:25-12:10 and Thursdays 13:00-14:45

First meeting: Thursday, September 27, 2018

Location: Classroom 8 on the third floor of the newly built International Academic Research Building (国際学術総合研究棟) located here:
https://www.u-tokyo.ac.jp/campusmap/cam01_01_07_j.html

Google Maps don't show the building yet, but it's next to "Library of Economics, University of Tokyo".

Course calalog codes: 291324-04, 5123041, 0704255. (In the catalog, the course title may sometimes show 'Applied Econometrics', for technical reasons. Hopefully this will not confuse you.)

Course content:
Deep learning in artificial neural networks is a collection of statistical methods that benefit from large datasets and parallel computing. Recently it led to remarkable progress in many domains of research. This course provides an introduction to the subject, including the latest research. The structure of the course is chosen with the aim to be useful to students with very different academic backgrounds.

Topics include: Optimization: backpropagation, stochastic gradient descent and its accelerated versions, second-order optimization methods. Supervised and semi-supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Neural network architecture: activation functions and their properties, layer patterns. Training neural networks: data preprocessing, weight initialization, gradient flow, batch normalization, regularization, practical aspects of GPU computing and distributed training. Hyper-parameter optimization, model ensembles, model compression. Transfer learning and fine-tuning. Spatial data modeling: convolutional networks, visualizing their internal data representations, susceptibility to adversarial examples. Sequence data modeling: recurrent networks, LSTMs, GRUs, and their convolutional alternatives, attention. Recursive data modeling: recursive neural networks. Natural language processing: word embedding and its visualization, neural machine translation, speech recognition and synthesis. Capsule networks. Unsupervised machine learning: variational autoencoders, adversarial networks, graphical models. Reinforcement learning: Q-learning, policy gradient methods and actor-critic methods, trust region policy optimization. Evolutionary strategies. Use of neural networks for designing and training other neural networks: neural architecture search, meta-learning. Hybrid computing combining advantages of neural networks and conventional computers. Use of deep learning for causal inference and counterfactual predictions. Privacy and ethical issues related to artificial intelligence.

Selected applications: econometric estimation of causal effects, solutions to game-theoretic models, economic time-series modeling, sentiment analysis, patient health outcome prediction, low-cost disease diagnosis, overcoming sensory loss with deep-learning technologies.
The course will include a first introduction to Python and to deep learning frameworks PyTorch, TensorFlow and Keras. The precise selection of topics for the course will be adjusted based on the students' interests.

Topics in Asset Pricing

Schedule: Wednesdays 13:00-14:45 and Fridays 13:00-14:45

First meeting: Wednesday, September 26, 2018

 Location: Classroom 513 on the 5th floor of the newly built International Academic Research Building (国際学術総合研究棟) located here:
https://www.u-tokyo.ac.jp/campusmap/cam01_01_07_j.html

Course calalog codes: 291325-01, 5123039, 0705027. (In the catalog, the course title may sometimes show 'Topics in Monetary Economics', for technical reasons. Hopefully this will not confuse you.)

Course content: Asset pricing theory and empirics - introduction and selected topics. Topics include: choice under uncertainty, static portfolio choice, capital asset pricing model, arbitrage pricing theory, stochastic discount factor, stock return predictability, consumption-based asset pricing, bond pricing and sovereign debt default risk, exchange rate determination, inter-temporal asset pricing, risk-sharing, asset markets with asymmetric information, household finance and its behavioral aspects, machine learning in finance. Each topic will be illustrated with real-world examples that will provide an intuitive understanding of when the models are useful and when they do not apply.


Data Science for Practical Economic Research

Schedule: Spring 2018, Tuesdays 10:25-12:10 and Wednesdays 14:55-16:40

First Meeting: Tuesday, April 10, 2018 (no meeting on Wednesday, April 11, 2018)

Location: Classroom 7 on the second floor of the newly built International Academic Research Building (国際学術総合研究棟) located here:
https://www.u-tokyo.ac.jp/campusmap/cam01_01_07_j.html
Google Maps don't show the building yet, but it's next to "Library of Economics, University of Tokyo".

Course calalog codes:
291324-02, 0704254, 5123038. (In the catalog, the course title may sometimes show 'Applied Econometrics', for technical reasons. Hopefully this will not confuse you.)

Course content:

This course is designed to help students use their time efficiently when performing economic data analysis.

Topics include: Data manipulation: dataset transformation, visualization, data cleaning, web data scraping, conversion of data for the purposes of econometric estimation. Supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Unsupervised machine learning: clustering, factor analysis, principal component analysis, independent component analysis. Semi-supervised learning. Distributed data representation: entity embedding. Nonlinear dimensionality reduction. Computational graphs and functional programming. Practical aspects of high-performance computing: GPU computing, cloud computing.

The course will include a first introduction to Python, R, and Mathematica, as well as PyTorch and TensorFlow. For specialized tasks other software will be introduced. Students are encouraged to bring to the class their own datasets, which could then be used for the purposes of instruction and practical demonstration.