## KSE MBAI Course: Advanced Machine Learning and Timeseries Modeling

**Description:**

Observing the world and compressing these observations into compact rules have been of great importance to humankind for ages. Nowadays we collect and generate a lot of data, so big that no human can analyze it. Machine learning is a field of science that is responsible for designing computer algorithms capable of learning important patterns directly from the large volumes of data without being explicitly programmed to. In this course, we are going to look into the principles and techniques that are at the core of machine learning. Topics will include notions of supervised and unsupervised learning; classification, regression, clustering and dimensionality reduction methods; deceptive effects of overfitting and ways to estimate models’ generalization power. Separately, we will look into time series modeling.

**Learning outcomes:**

- Review different classes of Machine Learning methods;
- Learn details of inner workings of some of the most important Machine Learning methods;
- Learn pros and cons and potential application domain of each method;
- Learn most common problems that are encountered when training Machine Learning models and ways to prevent them;
- Learn the fundamental differences between time-series and other types of data;
- Learn modeling methods specific to time-series.

**Exams & certification:**

After the successful completion of the course, the participants will get a certificate.

#### Speaker

**Dmytro Fishman**

Assistant of Data Science, Institute of Computer Science, University of Tartu

**Course outline:**

**Day 1**

Lecture 1: Supervised Learning, Classification Supervised Learning, Regression

Lecture 3: Supervised Learning, Overfitting, and CV algorithm

Lecture 4: Supervised Learning (practice)

Lecture 5: Introduction to time series modeling

Lecture 6: Time series modeling

**Day 2**

Lecture 1: Unsupervised learning, Clustering

Lecture 2: Unsupervised learning, Clustering

Lecture 3: Unsupervised learning, Dimensionality reduction

Lecture 4: Unsupervised learning, Dimensionality reduction

Lecture 5: Unsupervised learning, Dimensionality reduction

Lecture 6: Time series modeling

**Day 3**

Lecture 1: Deep Learning, artificial neuron, and feedforward path

Lecture 2: Deep Learning, backpropagation algorithm

Lecture 3: Deep Learning, feedforward, and backpropagation

algorithms (practice)

Lecture 4: Deep Learning, basics of convolutional neural networks

Lecture 5: Deep Learning, basics of convolutional neural networks

(practice)

Lecture 6: Time series modeling