برگزار شد

یادگیری ماشینی (machine learning)


مدرس :

آقای دکتر علی حبیب نیا

طول دوره ساعت :

24 ساعت

روز های بر گزاری :

شنبه ها

11 اردیبهشت لغایت 29 خرداد

ساعت بر گزاری :

17 الي 20

همراه با 10 ساعت حل تمرين (5 جلسه ، 2ساعته)


آقاي رهنما

روز های بر گزاری :

پنجشنبه ها

ساعت بر گزاری :

19الي 21


Finding patterns and relationships in large volumes of data are very useful in market research, business forecasting, decision support, and customer recommendation engines among other applications. Artificial intelligence methods that can lend itself to patterns and relationships in data will be introduced in this module. Applications of different deep machine learning algorithms will be discussed. Integration of these algorithms to business analytics frameworks will be demonstrated using real-world examples. This applied data science module aims to covers the theoretical, computational and statistical underpinnings of the machine learning techniques. The size, complexity, and diversity of data increase every day. This means we need new solutions for analyzing data. Big data and statistical learning methods provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models. Course demonstrations will be in Python, and for showcases and exercises, we make use of python scientific libraries. We also expose students to Google Colab so they can develop their coding skills by completing practical exercises on Colab. The data sets we will use for this course are from World Bank Group, Kaggle, Federal Reserve Economic Data, Google Finance, and several other resources. For the sake of learning, we will apply the algorithms and topics step by step to the problem, both in standard python libraries and from scratch

:Course Outline

The goal of this module is to give an applied, hands-on introduction to big data machine learning methods. At the end of the course, students will be able to read and understand theoretical papers on the subject, to implement the techniques themselves in Python by using NVIDIA RAPIDS libraries, and to apply the techniques to data used in economics and business. The style will be first to describe the theory and math behind algorithms and then demonstrate how to use RAPIDS library to create and run the models


An undergraduate-level understanding of linear algebra and probability analysis


 :Course title 

:Tentative Course Outline

 Python Programming & Essential scientific Libraries; (NumPy, pandas, matplotlib, statsmodels, scikitlearn, PyTorch)

 Fundamentals of Linear Algebra and Optimisation for Machine Learning in Python

 A Gentle Introduction to Computational Learning Theory

Types of Learning (Supervised, Unsupervised, and Reinforcement)

 Supervised Modeling: Regression vs Classification

 Parametric versus Semi and Nonparametric Models

 Kernel Methods: VC-Dimension, Support Vector Machines (SVM) • Tree-Based Models and Random Forest

 Neural Networks and Deep Neural Networks

 Model Selection and Feature Extraction

 Model Selection and Boosting: XGBoost

 Unsupervised Modeling and Clustering

k-means Clustering, Principal Component Analysis, Autoencoders, and Factor Analysis

 Reinforcement Modeling

 Inference and Learning

 Probabilistic Machine Learning

 Practical Advice for ML projects

This is only a tentative course outline. During the development, some topics will likely need to be expanded, or split into multiple sub-topics

:Main References

This is a restricted list of various interesting and useful books that will be touched during the course. You need to consult them occasionally

Gilbert Strang, Linear Algebra and Learning from Data, Wellesley Cambridge Press, 2019

 Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction,Springer, 2017. (Available Online)

 Shai Shalev-Shwartz, and Shai Ben-David, Understanding Machine Learning From Theory to Algorithms, Cambridge University Press, 2014. (Available Online)

 Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006


معامله گران و تحلیلگران بازارهای داخلی و بین المللی 

دانشجویان مالی و مهندسان کامپیوتر و علاقه مندان  بازارهای مالی  


تصویر برای دسته بندی علی حبیب نیا
علی حبیب نیا

دکتری آمار و اقتصادسنجی از دانشکده علوم اقتصادی و سیاسی لندن

عضو هیئت علمی دانشگاه ویرجینیاتک

علی حبیب نیا

دکتری آمار و اقتصادسنجی از دانشکده علوم اقتصادی و سیاسی لندن

عضو هیئت علمی دانشگاه ویرجینیاتک


  • شروع: شنبه 11 اردیبهشت 1400 - 05:00 ب.ظ
  • پایان: پنجشنبه 4 شهریور 1400 - 12:00 ق.ظ