Introduction to Machine Learning and Data Mining
Lecturers: Dmitry Ignatov
TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).
Homework 1: Spam classification.
Soft deadline (up to 10 points):
March 9 March 19
Hard deadline (-2 points):
March 15 March 25
Neural nets homework 1: logistic regression from scratch and first neural net on tensorflow's arithmetics
Neural nets homework 2: convolutional neural net on tensorflow and it's real-life application
Lecture on 23.01.2019
Practice: demonstration with Orange.
Lecture on 06.02.2019
Slides: Introduction to classification techniques (1-rule, kNN, Naive Bayes, Logistic Regression).
Practice: demonstration with Orange and scikit-learn.
Lecture on 22.02.2019
Practice with scikit-learn (kNN, Naive Bayes, Logistic Regression, basic quality metrics, cross-validation, error plots)
Slides: Decision trees. Entropy and information gain. ID3 algorithm. Gini impurity. Tree pruning.
Lecture on 06.03.2019
Slides: 1. Clustering. K-means, k-medoids, fuzzy c-means. The number of clusters problem and related heuristics. Hierarchical clustering. Density-based clustering: DBscan and Mean-shift.
2. Spectral Clustering for graph partition. Min-cut, Laplace matrix, Fiedler vector. Bipartite spectral clustering.
Lecture on 20.03.2019
Frequent itemsets and association rules. Apriori and FP-growth algorithms. Interestingness measures. Compact representations of frequent itemsets: closed itemsets and association rules. Applications: taxonomies of web-site users and contextual advertisement.
Lecture on 15.05.2019
Linear regression (simple regression, multivariative regression), RMS solution, gradient descent solution, Logistic regression, Multilayer neural net, chain rule, Cross-Entropy as loss function, introduciton in convolutional neural nets (convolution, pooling).
Applications: general purpose regression and classification tasks, computer vision.
Lecture on 29.05.2019
ConvNets regularization (L1+L2 weight decay, soft labels, early stopping), ConvNet debug(monitoring of metrics and tuning Learning Rate, checklist for debug), Image augmentations, Advanced tips&trics (pseudo-labeling, test-time augmentation, pretraining, Ensemble of nets with SGD), Common image-specific problems (Segmentation: Semantics and Instance, Detection, Identification; their metrics: IoU, mAP).
Applications: computer vision.