Maks Ovsjanikov

Coursework

Below are some of the courses I took during my college education, and I'm always looking to learn more :D

M.Res. Coursework

  • Artificial Intelligence: Intelligence Theory, Prolog, Fuzzy Logic, Advanced AI Techniques, Expert Systems, Artificial Neural Networks.
  • Natural and Artificial Vision: Artificial Retinas, Segmentation and Shape Recognition, Bio-inspired Neural Systems, Theory of Dynamical Systems, Cable Theory.
  • Image Processing: Image Compression, Mathematical Morphology, Adaptive Filtering, Advanced Tools for Image Procesing.
  • Deep Learning for Image and Video Processing: Recursive neural network, Auto-encoders and GANs, Geometrical Deep Learning, ConvNet, Supervised and Unsupervised Learning Techniques.
  • Architecture of Intelligent Systems: Systems Theoruy, Swarm Inteligence, Dynamics of Decision-making, Information Retrieval, Computational Models of the Brain, Neural Spiking.
  • Bio-inspired Systems: Humanoid Robotics, Animals and Humans Nervous Systems, Synaptics and Neuronal Placticity, Evolutionary Synthesis, Classical and Temporal Backpropagation Algorithms, Genesis of Rythmic Movements.
  • Advanced Optimization: Signal Processing, Optimal Linear Solutions, Adaptive Algorithms, RLS and LSE Algorithms, Approximation with a Multi-Layer NN, Simulated Annealing and Genetic Algorithms.
  • Learning and Adaptation: Data Analysis, Statistical Classifiers, Neural Networks, Q-Learning, Learning Maximization, Bio-inspired Learning.
  • Interactions of Electronic Systems with Living: Introduction to Electrophysiology, Electrodes and Physics of Interactions, Electroning and Living Interfaces, Brain-Machine Interfaces.
  • Affective and Social Robotics: Introduction to Affective and Social Cognition, Adabtive Behavior, Learning and Decision-making, Models of Emotion, Social Referencing, Verbal and Non-verbal Communications, Cognition Disorders.
  • Data Mining and Integration: Supervised Classification and Application to Prediction, Unsupervised Classification, Extraction of Patterns and Association Rules, Web Architecture.
  • Multimodal Human Machine Interfaces: Human-Machine Interactions Techniques, Immersive Systems, GUI, VR & AR, Tangible Interfaces, Cognitive Interactions.

M.Eng. Coursework

  • AI Ethics: Roboethics, Philosophy of AI, AI and Descrimination.
  • Work Ethics: Transparency, Communication, Work Attitude, Cooperation, Organisation, Efficiency, Respect, Teamwork.
  • Big Data & Cloud Computing: Database, Cloud, Storage, Compression, Memory Complexity, NoSQL, AWS.
  • Data Science Frameworks: Matplotlib, Seaborn, Pandas, Sckit-learn, Scipy, Ploty, Spacy.
  • Web Development: HTML, CSS, JavaScript, PHP, SQL.
  • Data Gathering: Web Scraping, Scrapy, BeautifulSoup, Selenium.
  • Time Series Forecasting: R Programming Language, Multiple Linear Regression, Classical Models for Time Series, Long Short-term Memory (LSTM), Efficiency Computing.
  • Bioinformatics: Sequence Analysis, Gene and Protein Expression, Analysis of Cellular Organization, Structural Bioinformatics, Network and System Biology, Bio-inspired Algorithms.
  • Deep Learning & Reinforcement Learning: CNNs, LSTMs, RNNs, GANs, RBFNs, SOMs, Autoencoders, Applications with Tensorflow.
  • GPU-CPU Programming & Parallel Computing: OpenMP for C, CUDA.
  • AI based Image Processing: Intorduction to Computer Vision, Signal Processing & Filters, OpenCV, Pillow, Introduction to PyTorch for Image processing.
  • Quantum Computing: Cryptography, Quantum Supremacy, Quantum Algorithms.
  • Natural Language Processing (NLP): Text and Speech Processing, Semantic Analysis, Morphological Analysis, Statistical Methods for NLP.
  • Metaheuristic Optimization: Search Algorithms, Parallel Metaheuristics, Nature-inspired Metaheuristics.
  • AI & Cybersecurity: Threat Exposure, Controls Effectiveness, Incidence Response, Fraud Detection, Breach Risk Prediction.
  • Reactive Programming: TypeSafe Stack, Scala, Play, Akka, Responsive Reactive Programming, Elastic Reactive Programming, Resilient Reactive Programming, Message Driven Reactive Programming.
  • Portfolio Management: Investment Anlaysis, Modern Portfolio Theory, Capital Asset Pricing, Investment Banking, Investment Model, Applications using R.
  • Microeconomics: Microeconomic Theory, Microeconomic Models, Market Structure, Game Theory.
  • Macroeconomics: Macroeconomic Models, Basic Macroeconomic Concepts, Macroeconomic Policy, Money Market.
  • Entrepreneurship & Product Management: Entrepreneurial Behaviours, Ressources & Financing, Market & Customer Research, Competitive Intelligence, Industry Analysis, Trends.
  • Intercultural Communication: Social Engineering, Verbal & Nonverbal Comuunication, Authentic Intercultural Communication, History of Assimilation, Cross-cultural Business Startegies, Globalization, Cultural Perceprtion.
  • Corporate Law: Corporate Structure, Corporate Finance, Corporate Governance & Balance of Power, Litigation.
  • Research Methodology: Data Gathering, Qualitative and Quatitative Analysis Methods, Bibliography, Reporting & Presenting.

BSc. Coursework

  • Real and Complex Analysis: Complex-valued Functions, Analytic Functions, Holomorphic Functions, Cauchy-Riemann Equations, Fourier Analysis, Formal Power Series.
  • Advanced Calculus: Infenitesimal Calculus, Limits & Derivatives, Differential Calculus, Integral Calculus, Smooth Infenitesimal Calculus, Advanced Measures.
  • Optimization:Standard Form, Slack Form, Duality, Variations, Classic Algorithms, Solvers.
  • Graph Theory: Enumeration, Subgraphs, Coloring, Route Problems, Network Flow, Visibility Problems, Covering Problems, Decomposition Problems, Graph Classes, Algorithms.
  • Differential Equations: Ordinary Differential Equations, Partial Differential Equations, Non-linear Differencial Equations.
  • Probability Theory: The Kolmogorov Axioms, Discrete Probability Distributions, Continuous Probability Distributions, Mesure-theoretic Probability Theory, Probability Application & Simulation.
  • General Topology: Topological Spaces, Algebraic Structures, Topological Invariants, Topological Data Analysis.
  • Linear Algebra: Vector Spaces, Matrices, Linear Systems, Endomorphisms & Square Matrices, Duality, Usage and Applications of Linear Algebra.
  • Number Theory: Elementary Number Theory, Analytic Number Theory, Algebraic Number Theory, Arithmetic Combinatorics, Applications of Number Theory for Cryptography.
  • Electromagnetism: Fundamental Forces, Classical Electromagnetism, Maxwell Equations, Wave Propagation, Nonlinear Phenomena.
  • Thermodynamics: Classical Thermodynamics, Statistical Thermodynamics, Chemical Thermodynamics, Laws of Thermodynamics.
  • Mechanics: Classical Mechanics, Quantum Mechanics, Introduction to Relativistic Mechanics.
  • Optics: Optical Systems, Superposition & Interference, Diffraction & Optical Resolution, Dispertion, Polarization.
  • Data Structures: Memory, Data Types, Usage & Implementation, Language Support.
  • Numerical Methods: Direct & Iterative Methods, Discretization, Numerical Integration, Numerical Stability, Functions Values Computing, Interpolation & Extrapolation, Solving Equations and Systems of Equations.
  • Monte Carlo Simulations: Integration, Stochastic Simulation, Inverse Problems, Markov Chains, Usage of Monte Carlo Methods.
  • Object Oriented Programming: Features of OOP, Design Patterns, OOP Application with Java.