Teaching

Courses:

  • Coding and Data Science for Accounting and Finance [FI505E]

This course will bring you from the very basics of coding in Python through to advanced data science topics like machine learning in order to equip you with the skills needed to play an active role in the new opportunities that these developments introduce to working in finance. This course is delivered in a non-conventional manner. There are 3 three-hour introductory sessions at the start of Semester 1, after which you will be expected to self-learn an online module to prepare you in the basics of Python coding. There will then be two consecutive six-hour sessions in November where you will apply your learning to the accounting and finance context in a Bootcamp style environment. Topics covered: • Introduction to data science in Finance • Fundamentals of Machine Learning in Finance • Coding in Python • Working with time series in Python • Machine learning with scikit-learn • Deep learning • Text mining

  • Neural Networks and Deep Learning for Finance [FI530E]

In this course, we teach the students a range of techniques to create scientific models from empirical data. The course consists of several lectures on data mining techniques with practical exercises in the class. Several lab exercises are designed to introduce the foundations and the application of deep learning in Finance. By finishing this course, students will learn how to work with Deep Learning models on big datasets. The course consists of 3 two-day blocks and final project presentation day. In the first block, students will learn the foundation of deep learning, and the second block focuses on applications of deep learning in Finance and managing machine learning projects. There is self-online learning between blocks. Each session includes lectures with following corresponding computer lab exercises. This course includes the following main topics: • Overview of Machine Learning methods • Fundamentals of Machine Learning in Finance • Python Programing • Neural Network: Feed-forward network • Recurrent network 6. Basic Idea of Deep Learning • Modern Practical Deep Networks • Application of Deep Learning in Finance • Deep Learning for Time Series data • Deep Learning for Textual Data

  • Data Science for Business

This course starts with the very basics of Python coding and works up modern advanced techniques such as machine learning and deep learning. The field of data science for business is the context for the class, and so applied business examples are the focus. The module has practicality at its heart, with students following the lessons using shared Python codebooks and implementing the techniques along with the professor.

  • Financial Data Infrastructure [FI532E]

It is essential to realize that today's students need formal training in areas beyond their central discipline; they need to know data management, computational sciences, and techniques. This means that students will have to understand financial data science much on a sufficient level. The future of social science involves the expansion of automation in all its aspects: data collection, storage of information, hypothesis formation, and experimentation. This course introduces tools for data analytics, machine learning for data analytics, and for exploring and visualizing data. It overviews the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool. It focuses on various Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. Students will also learn the machine learning techniques that are useful for data analytics and how to visualize data with various graphs and charts from a practice-oriented viewpoint. This course provides a reference guide -everything you need — including code samples — so you can get your hands dirty working with a real dataset and gain experience from financial projects. So after completing it, students will be able to work with different types of financial data structures.The course includes the following topics:
  • Big data and file formats (structured, unstructured, etc.), time series, text mining
  • Hadoop (an open-source platform for processing large datasets)
  • SQL and databases
  • Spark SQL in Python
  • Spark Machine Learning in Finance
  • Text mining
  • Financial Network


  • Business Network Intelligence

The Business Network Intelligence course helps students in scientific understanding of interactions between entities. The financial crisis of 2008–09 raised interest in how relationships within the financial system can magnify and spread shocks. The recent health crisis also has simultaneously underlined the importance of interconnection and interdependency between people in terms of their personal life or professional activities. Network models provide an appropriate framework for analyzing direct and indirect contagion effects through simulations or large networks' mathematical analysis. Networks can help to understand the complexity of our world, from daily life to strategic decisions. In this course, we will study the models and metrics which permit us to understand the complexity of networks. We will show analysis over large datasets of real social and technological networks like Twitter bitcoin networks. This course's final project is devoted to one of the most exciting topics in today's network analysis for business. The project will be based on the course's practical tasks, giving students hands-on experience with such assignments as network analysis, community detection, network centrality measurement.This course includes the following main topics:
  • Introduction to Networks and Random Graphs
  • Small World network
  • Centrality and Applications
  • Community Detection, Modularity, Overlapping communities
  • Information Cascades on Networks
  • Epidemic Dissemination on Networks
  • Cascades and Epidemics Applications


  • Business Textual Learning

The Textual analysis for the business course is intended for students who wish to obtain expertise in Natural Language Processing (NLP) technologies and applications in business. NLP technologies are of central importance in automating the analysis of text and speech databases and in enabling man-machine interactions through natural language. This course is designed to equip you with the essential tools to begin your adventures in analyzing text. This course will cover the basics of textual analysis and prepare you for expanding your analysis abilities. We dive into topic modelling, all while providing thorough examples that can be used to kick start your Bootcamp project. This course helps students master a skill. This course covers a wide range of tasks in text analysis from basic to advanced: sentiment analysis, summarization, to name a few. Upon completing, students will be able to recognize text analysis in their day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project of this course is devoted to one of the hottest topics in today’s text analysis for business. The project will be based on practical assignments of the course, that will give students hands-on experience with such tasks as text classification, named entities recognition.The course includes the following topics:
  • Data preparation for Text Mining
  • Word association mining & analysis
  • Opinion mining & sentiment analysis
  • Topic mining & analysis
  • Application of Text mining in business

Prior Years' Courses:

Machine Learning in Finance, Management and Accounting (PhD Program)

In this course, we teach the students a range of techniques to create scientific models from empirical data. The course consists of several lectures on data mining techniques with practical exercises in the class. Several lab exercises are designed to introduce the application of data mining in social sciences. Students will learn how to work with big datasets and apply some advanced techniques in their own research field. After completing the course, students shall be able to independently draft an academic paper on key issues of social sciences by using machine learning techniques.
Course content:• Introduction to data mining: Supervised learning (prediction, classification), Unsupervised learning (associationanalysis, clustering), Regression, hypothesis test, descriptive statistics (Overview and background)• Introduction to R• Ridge regression, Principal components regression (PCA) Partial Least Squares regression (PLS)• Splines function, Kernel smoothers, Generalized Additive Models (GAM)• Classification methods Support Vector Machine (SVM), CART and MARS• Tree-based methods and Random Forests• Clustering methods• Neural networks and Deep learning• Text mining• Application of machine learning ( Finance, Business, Accounting, )

Mathematics : Bachelor Program

Teacher Assistance 2012-2016:

Mathematics for PhD ProgramStatistics and Econometric (Master of Finance )The principle of Statistics (Master in Management)Principles of Finance (Master of International Business)Market Estimation and Forecasting (Master of Finance)Econometric (Bachelor's Program (BSc))Advanced Derivative ( Master of Quantitative Finance)