Машинное обучение в Питоне

This course introduces the students to the elements of machine learning and deep learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods, and topics in neural networks. Students apply Python programming language and popular packages, such as pandas, scikit-learn and TensorFlow, to investigate/visualize datasets and develop machine learning models to solve data-driven regression, classification and unsupervised problems

  • Общеуниверситетский факультатив на платформе "Онлайн-образование в НИУ ВШЭ"
  • 21 сентября 2021 года по вторникам с 18:10 до 19:30
  • 15 недель (4 кредита) 
  • Лекции: 28 часов; семинары: 28 часов; самостоятельная работа: 96 часов
  • В онлайн-формате (читается на английском языке)
  • Сертификат

О курсе

This course is based on a world-famous book of Introduction to Statistical Learning written by Stanford University's faculty. The text gently and in plain English introduces learners to the world of machine learning. Students gain expertise to solve their own data problems and help attain and measure greater efficiency and automation in the company's and personal data-driven projects.

Most assignments are quizzes with conceptual questions, analytical questions, interpretation of modeling plots, analysis of machine learning algorithms, and some Python coding. Some questions may ask students to evaluate/analyze/solve mathematical formulas, which are behind all machine learning models. Students will also be exposed to Python programming language via machine learning examples in seminars, but are not expected to solve heavy programming or mathematical assignments. Some knowledge of Python is helpful, but not necessary for OOC students. In seminars, practical applications of Python to the problems will be demonstrated. Students will have an opportunity to advance their knowledge using learned models, Google Colab coding environment, and popular packages such as pandas, scikit-learn and TensorFlow

Цели курса


01

Develop an understanding of the process of machine learning from data, which includes exploratory data analysis, visual and analytical understanding interpretation of patterns and guesstimating the appropriate machine learning model


02

Familiarize students with a wide variety of algorithmic and model based methods to extract information from data. This includes a list of linear and non-linear models as well as ensembles of these


03

Teach students to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation

Вы научитесь

1. Evaluate and solve a wide array of regression, classification and clustering problems with tools from statistical and machine learning fields using Python programming language

2. Exploratory data analysis in Python

3. Machine learning model training, evaluation, tuning and comparison in Python

4. Data and model visualization in Python

Программа обучения

1. Academic Integrity, Honor, Ethics

2. Review of Calculus, Linear Algebra, Probability, Stats, Python, Colab

3. Introduction to Statistical Learning

4. Linear Regression and K Nearest Neighbor (KNN)

5. Classification: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, KNN

6. Resampling Methods. Cross Validation (CV), Bootstrap

7. Linear Model Selection and Regularization

8. Non-linear Regression

9. Decision Trees, Bagging, Random Forest, Boosting

10. Support Vector Machines (SVM)

11. Clustering and Dimension Reduction: k-Means, Hierarchical Clustering (HC), DBSCAN, PCA

12. Artificial Neural Networks (ANN) and Introduction to Deep Learning

13. Recurrent Neural Networks (RNN), Long-short Term Memory (LSTM)

14. Convolutional Neural Networks (CNN)

15. Deep Generative Models and Autoencoders

Преподаватели

Мельников Олег Борисович

Приглашенный преподаватель: Факультет компьютерных наук / Департамент больших данных и информационного поиска

Макаров Михаил Сергеевич

Преподаватель: Факультет Компьютерных наук / Департамент больших данных и информационного поиска, Senior Data Scientist в компании 3PM Solutions

 

Тихонова Мария Ивановна

Преподаватель: Факультет Компьютерных наук/Департамент больших данных и информационного поиска

Для кого

This course is designed for sufficiently prepared students ready to advance into machine learning for the purpose of self-improvement, career switch or growth into data science or for application of machine learning in the workplace. It is important that the students have a reasonably fresh experience with basic college calculus (specifically, differentiation techniques), linear algebra (operations on matrices), probability and statistics, basic computing coding and a good understanding of English language, since most of the course material will be in English

Документ об окончании

После успешного освоения материалов курса выдается сертификат установленного НИУ ВШЭ образца

 

 

Формат обучения


Лекции

via Zoom


Семинары

via Zoom


Консультации

Saturday evenings via Zoom


Промежуточный и итоговый контроль

Auto-generated and auto-graded weekly quizzes


Комментарии

Active participation and video meetings are important in this course and are graded


Стоимость и условия


8 тыс. ₽

Полный доступ к материалам курса + сертификат

Подробнее: публичная оферта

 


Бесплатно

Только лекции