Advanced Machine Learning

Overview

Syllabus

Courses

• 2 reviews

  5 weeks

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  This online course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.
  
  Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to the attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.
  
  Do you have technical problems? Write to us: [email protected].
• 0 reviews

  6 weeks

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  People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.
  
  When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money.
  
  In this online HSE course we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques.
  
  We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases can be found with Bayesian methods.
  
  
  Do you have technical problems? Write to us: [email protected]
• 2 reviews

  5 weeks

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  If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.
  
  In this course, you will learn to analyse and solve competitively such predictive modelling tasks.
  
  When you finish this class, you will:
  
  - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.
  - Learn how to preprocess the data and generate new features from various sources such as text and images.
  - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions.
  - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data.
  - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them.
  - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance.
  - Master the art of combining different machine learning models and learn how to ensemble.
  - Get exposed to past (winning) solutions and codes and learn how to read them.
  
  Disclaimer : This is not a machine learning online course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them.
  
  Prerequisites:
  - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM.
  - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.
  
  
  Do you have technical problems? Write to us: [email protected]
• 3 reviews

  2 days 12 hours 12 minutes

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  Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. We recommend taking the two previous courses in the specialization, Introduction to Machine Learning: Supervised Learning and Unsupervised Algorithms in Machine Learning, but they are not required. College-level math skills, including Calculus and Linear Algebra, are needed. Some parts of the class will be relatively math intensive. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image by Ryan Wallace on Unsplash.
• 3 reviews

  5 weeks

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  Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.
  
  The goal of this online course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In the course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.
  
  Do you have technical problems? Write to us: [email protected]
• 0 reviews

  5 weeks

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  The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique.
  
  In this online course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.
  
  Do you have technical problems? Write to us: [email protected]
• 2 reviews

  6 weeks

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  Welcome to the Reinforcement Learning online course.
  
  Here you will find out about:
  
  - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
  --- with math & batteries included
  
  - using deep neural networks for RL tasks
  --- also known as "the hype train"
  
  - state of the art RL algorithms
  --- and how to apply duct tape to them for practical problems.
  
  - and, of course, teaching your neural network to play games
  --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
  
  Jump in. It's gonna be fun!
  
  Do you have technical problems? Write to us: [email protected].

Taught by

Alexander Guschin, Alexander Novikov, Alexander Panin, Alexey Artemov, Alexey Zobnin, Andrei Ustyuzhanin, Andrei Zimovnov, Anna Kozlova, Anna Potapenko, Anton Konushin, Daniil Polykovskiy, Dmitry Altukhov, Dmitry Ulyanov, Ekaterina Lobacheva, Evgeny Sokolov, Marios Michailidis, Mikhail Hushchyn, Mikhail Trofimov, Nikita Kazeev, Pavel Shvechikov and Sergey Yudin

Tags

• russia