Complex Logic

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Graduates of this special program will have general expertise in various areas.

During the first semester, we focus on the essential fundamentals, such as basic concepts of math and programming, which are the same for all programs.

In the Math & Statistic course, students will learn about discrete mathematics and the basics of probability theory. Algorithms & Data Structures covers the basic algorithms on graphs, data structures, and recurrence relations. Also, during the first semester, students learn how to process and analyze data, and build simple models based on this data in the Data Science course. In the Advanced Programming Languages course, students will learn about programming languages and their fundamental differences.

During the second semester, in the Product Strategy, Vision, Marketing, and Innovation course, students will learn how to build their own project, create financial forecasts, and develop the project for the entire product. Data Science (part 2) will give an insight into machine learning, where students will try some of the most popular concepts. Networks & Clouds offers fundamentals of networks, storage, and security. In the System Architecture course, students will get familiar with the main concepts of systems architecture. 

Semester 1

Math & Statistics

  • Set Theory

  • Relations and Functions

  • Logic and Induction

  • Sequences

  • Number Theory

  • Combinatorics

  • Recurrence relations

  • Counting techniques

  • Graph Theory

  • Graphs and Numbers

  • Growth of Functions

  • The Probability of an Event

  • Discrete Random Variables

  • Continuous Probability

  • Conditional Probability

  • Distributions and Approximation

  • Sampling Theory

  • Statistics

  • Advanced Algebra

Algorithms and Data Structures

  • Sorting and Searching

  • Graphs (basics)

  • BFS and DFS

  • Dynamic Programming

  • LIS, LCS and other 2D problems

  • Heaps

  • Dijkstra, Floyd, Bellman-Ford

  • DFS applications

  • Knapsack problem

  • Dynamic programming over subsets

  • Amortized Time Complexity (Queue via Stack)

  • Disjoint Set Union

  • Dynamic programming on a tree

  • Data Structures. RSQ/RMQ, Sqrt Decomposition, Sparse Table

  • Minimum Spanning Trees

  • Matching

  • Segment Trees

  • Trie

  • Binary Search Trees

  • LCA

  • What is NP and how is it useful?

  • Strings

Data Science

  • AI Introduction

  • Machine Learning Basics

  • ML as a task of optimization

  • Hyperparameters

  • Probabilistic Approach in ML

  • Models Validation

  • Linear regression

  • Logistic regression

  • Decision tree

  • SVM algorithm

  • Naive Bayes algorithm

  • KNN algorithm

  • K-means

  • Random forest algorithm

  • Dimensionality reduction algorithms

  • Gradient boosting algorithm and AdaBoosting algorithm

  • Data Preprocessing

  • Time Series Forecasting

  • Ranking task

Advanced Programming Languages

  • The dawn of programming

  • Grammars

  • Programming Language Spectrum

  • Programming paradigms

  • Programming language syntax

  • ANTLR

  • Compilation and interpretation

  • Binding and memory management

  • Mutable vs. immutable data structures

  • Data-Oriented Programming

  • LLVM

  • Linking

  • Introduction to Clojure

  • Clojure macros

  • Languages for data

  • Constraint programming

  • Lua

  • Elixir

  • Learning and coding with LLMs

Semester 2

Networks & Clouds

  • What is DevOps?

  • OSI model and networking

  • Advanced networking and Web APIs

  • SSH and basic Linux CLI commands

  • Linux CLI commands and Bash scripting

  • Cloud Computing Explained

  • AWS: The Leading Cloud Service Provider

  • Containers: Virtual Machines of the Future

  • Advanced Docker

  • Introduction to AWS Lambda

  • AWS RDS

  • CICD Tools Review

  • CDN and Website Protection: Cloudflare & AWS CloudFront in Action

Systems Architecture

  • C++ - syntax, OOP basics, UB, numbers

  • Rust - syntax, programming paradigms, safe/unsafe, comparison with C++

  • Memory Management. Stack and heap memory, variable sizes, Ownership, smart pointers

  • Memory Management. Core containers, iterators, internal implementations. Error handling

  • Syscalls, Processes, Scheduling

  • System limits - rlimit, cgoups, Linux namespaces, seccomp

  • Multithreading. Mutex, atomic operations. Condition variables, channels

  • Multithreading. Async functions, coroutines, green threads

  • Observability. Metrics, Prometheus, Grafana. Logging, telemetry, alerts

  • Distributed Systems

  • Data encoding: UTF-8, big-endian vs. little-endian, prefix encoding, possibly Huffman trees

  • Deserialization: JSON, Protobuf, etc.

  • Traffic balancing, Nginx

  • Databases, message queues

  • Assembler (Instructions, RISC, CISC, x86, Intel, asm basics)

Data Science (part 2)

  • Big Data, Hadoop, Spark

  • Artificial Neuron Model

  • Multilayer ANN 1: Hyperparameters, Regularization, Training Process

  • Multilayer ANN 2: Adam, Dropout, Weight Initialization, Batch Normalization

  • Convolutional Neural Networks (CNN)

  • Recurrent Neural Networks (RNN) – LSTM, GRU

  • Neural Network Architectures – GAN, Seq2Seq, Autoencoder

  • Attention and Transformers

  • Generative AI: Principles and Types of Models

  • Generative AI: Prompt Engineering, Fine-Tuning, LLM Inference

  • Approaches to Building RAG Based on LLM

  • AI Ethics

Applied GenAI

  • Introduction to Generative AI

  • LLM Creation Pipeline – Data Preparation & Tokenization

  • LLM Creation Pipeline – Architectures & Mitigating Hallucinations

  • Prompt Engineering & Retrieval-Augmented Generation (RAG)

  • Project 1 Kickoff: Building a Text Generation App

  • Project 1 Implementation: Enhancing the Text Generation App

  • Evaluating Text Generation Outputs

  • Introduction to RLHF & DPO

  • Fine-Tuning with RLHF/DPO

  • Project 2 Kickoff: Building an Interactive Conversational Agent

  • Project 2 Implementation: Enhancing the Interactive Agent

  • Introduction to Image Generative Models & Data Augmentation

  • Project 3: Hands-On with Image Generation for Data Augmentation

  • Ethical Considerations, Safety, & Future Trends in Generative AI

  • Final Capstone Project Walkthrough & Wrap-Up

Check admissions to learn if you are eligible and enrol.