Lectures related to Statistical Learning Theory: Crash Course

Real Functions: Definitions and Properties

Explores real functions, covering parity, periodicity, and polynomial functions.

Probability and Statistics

Covers mathematical concepts from number theory to probability and statistics.

Probabilistic Functions: Free Fields and Random Variables

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Proof of Empirical Process Deviations

Covers the proof of bounds on empirical process deviations, focusing on mathematical derivations and inequalities.

Relations in Computer Science

Explores the properties of relations in computer science, including equivalence relations and the partition of a set.

Course Overview: Teaser on Course Contents

Offers an overview of propositional and predicate logic, sets, functions, relations, algorithms, Swiss cities, sorting tables, Covid infections, poker hands, and prime numbers.

Introduction and Notations

Introduces advanced probability concepts, focusing on key notations and conventions.

Taylor Series: Convergence and Applications

Explores Taylor series convergence and applications in approximating functions and solving mathematical problems.

Entropy and the Second Law of Thermodynamics

Covers entropy, its definition, and its implications in thermodynamics.

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Functions and Periodicity

Covers functions, including even and odd functions, periodicity, and function operations.

R Programming: Conditions, Loops, Functions & Graphics

Covers conditions, loops, functions, and graphics in R programming with practical examples.

Advanced Analysis II: Differential Equations and Timers

Discusses advanced analysis concepts, focusing on differential equations and timers in microcontrollers.

Excel Upgrade: Advanced Functions and Data Analysis

Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.

Discrete Mathematics: Logic, Structures, Algorithms

Covers the basics of discrete mathematics, focusing on logic, structures, and algorithms for computer systems.

Probability Theory: Integration and Convergence

Covers topics in probability theory, focusing on uniform integrability and convergence theorems.

Transforms of the Place

Explores the intuition behind transforms of the place and addresses audience questions on integral calculations and function choices.

Comparison Series and Integrals

Explores the relationship between series and integrals, highlighting convergence criteria and function examples.

Python Modules and Functions

Covers Python libraries, modules, scoping, functions, lambdas, and practical examples.