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Repetition and perseverance is the key to learning and success !!
How do we learn ?
Hermann Ebbinghaus, a German psychologist, described in 1885 the exponential nature of forgetting. In his study (of which he himself was also a participant), subjects memorize a list of meaningless, three-letter words. Ebbinghaus tracked how quickly his subjects forgot the words, and this is now known as the Ebbinghaus or Forgetting Curve, which can be concisely approximated as R=e-t/S, where R is the amount of information retained, S is the relative strength of memory, and t is the elapsed time. 
Overcoming the Ebbinghaus Curve: Amount of information retained changes upon each successive time the information is reviewed. We can overcome the forgetting curve simply by reviewing in a systematic fashion:
- 10 min-After class by completing, organizing, and comprehending by rewriting or typing notes (e.g., Cornell right column)
- 24 hours-Next day, by rereading notes, condensing them to main ideas, and creating questions (e.g., Cornell left column)
- 1 week-Before class the following week or earlier, by reviewing and self-evaluating your recall
- 1 month-When you review material about a month after reading it, you often draw new insights from it, and at that point, may be able to retain that information for the rest of your life
Does a study of three-letter words apply to Logic and Computer Science?
One can argue that in computer science, the problem is with applying, not memorizing, and it is an issue of logic and reasoning, and not remembering or forgetting!All people learn using three mechanisms: habituation, sensitization and associative learning.
While we may think that our learning needs and models are significantly different, underlying learning mechanisms are still the same.
The following quote, attributed to William Glasser, articulates what portion of material we absorb, depending on what activity we perform with it: "We learn 10% of what we read, 20% of what we hear, 30% of what we see, 50% of what we both see and hear, 70% of what is discussed with others, 80% of what we experience personally, and 95% of what we teach someone else."
The take-away point is that computer science is learning, like other forms of learning. We can always learn better by improving how we learn. PRACTICE ! PRACTICE ! AND PRACTICE !
Cours disponibles
The objective of this course is to introduce students to the basics of imperative programming which is one of the most effective paradigms of modern programming. Thus, the theme of algorithmic will be discussed to prepare the continuation of the course. Indeed, an algorithm expresses the logical structure of a solution to a given problem and this independently of any language and any machine. Algorithmic is one of the key steps in teaching programming. Then, it is a matter of applying the knowledge approached in algorithmic through the mastery of a programming language. This is precisely the purpose of this second part of this module which aims to equip students with the background necessary to master a given programming language, in this case the C language, which is at the base of many others. Our objectives are summarized as follows:
- Learn the basic concepts of algorithmic
- Be able to implement these concepts to analyze simple problems
- Mastering abstraction mechanisms in order to analyze a problem and systematically design correct and adequate algorithms and programs
- Implement, using the C language, the algorithms solving a particular problem
- Master the basics of modular programming
The objective of this module is to deepen the study of the imperative programming through the use of advanced constructions of the imperative language seen in I1101. The student must be able to apply the concepts seen in this course to create an application solving a complex problem in the form of modules.
The intended learning outcome of this course is to give good knowledge of graph theoretical concepts, and to practice how to use them in mathematics, natural science and computer science.
After completing the course, students should be able to:
- know some important classes of graph theoretic problems;
- be able to formulate central theorems about trees, matching, connectivity, colouring and planar graphs;
- be able to describe and apply some basic algorithms for graphs;
- be able to use graph theory as a modelling tool.
- to give students a thorough understanding of the Python programming language and it's rich set of libraries;
- to expose students to applications where Python programming is effective, and
- to introduce students to pros and cons of scripting vs. compiled programming languages.
It has easy to understand syntax which allows programmers to develop programs faster and be more efficient. Python has proven to be equally as useful for small scripts as well as large scale software systems.
Due to Python’s ease of use, students will gain expertise with many details of the language as well as programming fundamentals in a short period of time.
Students learn to develop applications where complete implementation requires high skills in structured programming domain. The main objectives are to specify, conceive and develop modules implementing the fundamental data structures and to choose the most appropriate data structure to represent data in a given problem and to allow its resolution by applying efficient and optimal operations.
This course explores advances in algorithm design, algorithm analysis and data structures. The primary focus is on advanced data structures. Topics include advanced trees structures, disjoint sets, heaps, algorithm design techniques, data structures for strings and linear programming algorithms. Finally, we discuss NP-completeness.
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