Course materials
Lecture 1: Introduction to Machine Learning, Questionnaire 1
Lecture 2: Linear regression. Loss function. Gradient Descent, Questionnaire 2, Practice 1
Lecture 3: Linear classification methods
Lecture 4: Metrics. Over/underfitting. Data splitting. Regularization. Bias-dispersion dilemma, Questionnaire 3
Lecture 5: Feature representations. Feature selection, Questionnaire 4, Practice 2
Lecture 6: SVM, KNN, Naive Bayes
Lecture 7: Decision Tree. Ensembles
Lecture 8: Introduction to Neural Networks, Questionnaire 5
Lecture 9: Software libraries for NN development
Lecture 10: Deep learning. Transfer learning. Multitask learning
Lecture 11: Clustering
Lecture 12: Unsupervised learning. AutoEncoders. Word2Vec
Lecture 13: Convolutional neural network
2023-results
Useful materials
Books
- Pattern Recognition and Machine Learning - Bishop C. M, Springer, 2006
- Deep Learning-Ian Goodfellow, Yoshua Bengio, Aaron Courville, The MIT Press, 2016.
Online courses
- Supervised Machine Learning: Regression and Classification - Andrew NG’s online course, Stanford University.
- Machine learning - Konstantin Vorontsov’s course, Yandex School of Data Analysis.
Additional materials
- machinelearning.ru - information resource dedicated to machine learning, pattern recognition and data mining.
- Deep Learning Guide by YerevaNN.
Python
A brief overview of the basis.
Jupyter Notebook for interactive computing.
Interactive tutorial in English.
Pandas tutorial 1, tutorial 2.