This course is intended for engineering students (ENSA, ENSAM, or others) and students of science faculties (S4) and all those who want to master a programming language. This course covers all fundamental and advanced aspects of algorithms and programming languages. I guide you in learning and mastering the following languages: C, C++, Java, Python, Prolog, Scheme, Lisp, and others.

I give computer courses. Students concerned: - Students following a Computer Science License (or Master). - Engineering students (Computer Science or Data Sciences majors). - Individuals who want computer training. Here are the courses I can provide: - Basic algorithms: variables, constants, data types, operators, expressions, input/output instructions, conditional instructions, loops, subalgorithms, arrays, pointers, files, recursion. - Advanced algorithms: proof of algorithms, complexity analysis, search algorithms, sorting algorithms, divide and conquer technique, greedy technique, dynamic programming technique, probabilistic algorithms. - Data structures: lists, stacks, files, hash tables, binary trees, garphs. - Graph theory. - Theoretical computer science: formal languages, finite automata, regular expressions, regular languages, stack automata, context-free grammars, algebraic languages, Turing machine, decidability and undecidability, complexity, NP-complete problems. - Compilation techniques: lexical analysis (Flex), syntactic analysis (LL(1), LALR, Bison analysis), semantic analysis, intermediate code generation, intermediate code optimization, target code generation. - Programming languages: C, C++, Java, Python, Prolog, Lisp or others. - Object-oriented modeling with UML: UML notation, class diagram, use case diagram, state transition diagram, defrosting diagram, collaboration diagram, package diagram, component diagram. - Object-oriented programming: encapsulation, abstraction, attributes, methods, classes, association, composition, inheritance, polymorphism. - Web technologies: HTML, CSS, JavaScript, PHP or others. - Metaheuristics: local search, tabu search, simulated annealing, genetic algorithms. - Software engineering: software quality, software life cycles, SCRUM, XP, RUP. - Artificial intelligence: problem solving through research, AI games, Constraint satisfaction problems, expert systems, Prolog, Lisp. - Data Mining: life cycle of a data mining process, association rules, sequence analysis, classification, clustering, decision tree. - Text Mining: life cycle of a text mining process, information extraction.

I give introductory or advanced algorithmic and programming courses for: Engineering students (API classes). University students (S1, S2 of scientific sectors: SMA, SMI, SMP, SMC, SVT). The beginner level program: introduction to computer science, concept of algorithm, variables and data types, operators and expressions, primitive instructions, tests, loops, sub-algorithms, tables, pointers and files. The advanced level program: proof of algorithms, analysis of the complexity of algorithms, search and sorting algorithms, algorithmic strategies (divide and conquer technique, greedy algorithms, dynamic programming, randomized algorithms). Data structures: linked lists, stacks and queues, hash tables, binary trees and graphs. Programming languages: Java, C, C++, Python (or others). Correction of exercises (TD or TP), old exam subjects. Advanced courses in algorithms and data structures. Training courses for people who want to become computer science teachers.

I give algebra and analysis lessons to engineering school students (API) and university students (Semesters S1, S2, S3 and S4 of all scientific sectors). I also offer revision and support courses, refresher courses, corrections of exercises, homework and exams. My objective is to help the student to understand mathematical concepts in a very simple way (and without too much abstraction) and to know how to use the results of the course (theorems) to solve algebra and analysis exercises. . Programs offered: - General algebra: 1 Matrices, Systems of linear equations, Determinants, Euclidean vector spaces, Orthogonality, General vector spaces, linear transformations, Values and eigenvectors, Diagonalization. - Analysis: Topology of R, Numerical sequences, Limits and Continuity, Differentiability, Sequences of functions, Integral calculation, Differential equations, Topology of R^n, Functions with several variables, Generalized integrals, Improper integrals, Numerical series Series of functions, Series integers, double and triple integrals. - Numerical analysis: Elements of matrix analysis, Foundations of scientific computing, Numerical linear algebra, Iterative methods for solving linear and nonlinear equations, Eigenvalue approximation and eigenvectors, Polynomial interpolation, Numerical integration, Orthogonal polynomials in approximation theory, Numerical resolution of ordinary differential equations, Parabolic and hyperbolic transient problems.