IT advanced — list of classes

(type: L= lecture, P = practice)

# type topics
1 L General architecture of a computer. The Von Neumann machine. The representation of information in a computer (hardware).
2 L The machine representation of numbers and errors (logical). The binary numbers, the hexadecimal numbers. Encodings.
3 L The operating system in general. MS-DOS, Linux/Unix. Graphical Operating Systems: MS Windows, Apple MacOS, KDE, Gnome. Programming environments: editor, debugger, compiler, interpreter.
4 L Networking protocols, layers, packets. TCP/IP protocol. World Wide Web, HTML, browsers. Search engines. Other protocols: email, FTP, servers, clients.
5 P Computer Lab practice on internet resources: academic resources, advanced search engines practice.
6 L Main programming language types: machine code, low-level, high-level,

interpreted, compiled. Syntax and semantics.

7 L Programming environments: editor, debugger, compiler, interpreter. Software design techniques. Algorithms.
8 L Elements of the C language: keywords and syntax, control flow statements, data types.
9 L The Python language. Keywords and syntax. Control flow statements. Data

types.

10 P Computer Lab practice on Python programming: first simple algorithms.
11 L Python Libraries: NumPy, SciPy. Examples of mathematical functions,

mathematical data generation and manipulation. Examples of simple

scientific plots.

12 P Computer Lab practice on Python programming: first scientific applications.
13 P Computer Lab practice on Python programming: more scientific applications.
14 L Python data processing. Examples of data plotting and representation: 2D

plots, 3D plots, annotations.

15 P Computer Lab practice on Python programming: scientific data plotting.
16 L Python datafiles input and output. Text files and binary files. Strings

Manipulation. Regular Expressions.

17 P Computer Lab practice on Python programming: data files input and output.
18 L Numerical analysis. Direct methods and iterative methods. Precision. Example algorithms: sorting, searching.
19 L More numerical analysis. Root-finding, linear equation systems solving, functions interpolation, functions integration. Discrete Mathematics.
20 P Computer Lab practice on Python programming: applications on Numerical analysis and discrete mathematics.
21 P Computer Lab practice on Python programming: applications on Numerical analysis and discrete mathematics.
22 L Statistics and Probability. Fundamentals of statistics. Fundamentals of probability theory. Bayes theorem.
23 L Data analysis. Classification problems. Regression Problems. Elements of neural networks. Examples of applications to classification and regression Problems.
24 L Definition of information. Information compression: typical set, source coding theorem.
25 L Information transmission: the noisy channel, error correction, noisy-channel coding theorem.
26 L Complexity theory. Definition of computational complexity. Complexity models: oracle model, circuit complexity, time complexity. Some example of complexity classes. Description of the “P vs NP” problem
27 P Computer Lab practice on Python programming: Examples from the course.
28 P Computer Lab practice on Python programming: Examples from the course.
29 P Computer Lab practice on Python programming: Examples from the course.
30 P Computer Lab practice on Python programming: Examples from the course.
31 P Computer Lab practice on Python programming: Examples from the course.
32 P Computer Lab practice on Python programming: Examples from the course.