Skoltech is an international graduate research-focused university that was founded by the group of world-renowned scientists in 2011. Skoltech's curriculum focuses on technology and innovation, offering Master's programs in 11 technological disciplines. Students receive rigorous theoretical and practical training, design their own research projects, participate in internships and gain entrepreneurial skills in English. The faculty is comprised of current researchers with international accreditation and achievements.

• High-Performance Computing (HPC)
• Introduction: history of HPC, terminology of HPC, main areas of applications, current trends
• Compromise between scalability and convergence rate
• Memory greedy vs CPU/GPU greedy methods
• A list of most important equations we solve with use of HPC, corresponding models and most popular numerical methods
• Three main sources of errors: numerical (solvers), error of the model (due approximation of “reality”), statistical (due to imperfect sampling)
• Scalability of different methods (e.g., spectral or local)
• Balance between accuracy and computational efficiency
• Advanced sampling techniques (Monte-Carlo methods)
• Random number generators (what makes a good number generator)
• MC integration, convergence
• Convergence of MC methods

Prerequisites:N/A

• Foundations of Convex Analysis
• Black Box Convex Optimization. Lower bounds
• Mirror descent
• Fast Gradient Method. Universal method.
• Stochastic programming
• Coordinate descent

Prerequisites:
Real analysis (calculus), basics of functional analysis (Hahn-Banach theorem and consequences) linear algebra, basic probability and tight concentrations (martingales, Azuma inequality), statistics (maximum likelihood estimation, normal regression), game theory and algorithms.

• Lecture I –Electrochemical systems
• Lecture II –Interactions in an electrochemical systems
• Lecture III –Double layer
• Lecture IV –Electrochemical cells & kinetics
• Lecture V –Energy applications: supercapacitor, lithium ion battery

Lab works

• Electrochemical window, reference electrode, surface reactions
• Supercapacitor

• Launch services and markets
• Satellite manufacturing and operations, including sensors and payloads
• Space data products
• Space communications and navigation services
• Space science payloads and missions
• Military space
• Human spaceflight, programs and policies

For each one, we will discuss the organization of the sub-sector, what value it produces, how it is funded, the commercial and governmental organizations that participate, how it is regulated, and technology barriers

Part 2: Technologies:

• Technology readiness, and sources of technology, technology planning
• Launch technologies and options
• Satellite technologies
• Payload technologies (may need to do in parts)

Part 3: Organizations

• Space sector organizations (verticals and horizontals)
• “New Space”
• Space agencies and other international agents (NASA, ESA, etc.)

Prerequisites:
N/A

• Basic RL techniques: k-means, PCA, sparse coding
• Large-scale retrieval systems, quantization and product quantization, inverted indexing
• Low-dimensional embeddings, metric learning
• Large-scale discriminative learning, stochastic gradient descent
• Deep networks, backpropagation
• Autoencoders, deep stochastic neighborhood embedding (SNE), Siamese architectures
• Convolutional neural networks (CNN)
• Applications of CNNs in computer vision
• Recurrent neural networks (RNN), long short-term memory (LSTM) networks and speech recognition
• Applications in natural language processing, word2vec
• Applications of RLDL in molecular biology/genomics

Prerequisites:
Machine Learning class – a must, Optimization methods class – desirable, good coding skills in Python or Matlab – a must, good coding skills in C++ – desirable.
High-Dimensional Statistical Methods.