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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 !

Passer cours disponibles

Cours disponibles

BS1

Algorithmic & Programming

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

BS2

Functional Programming - Python

This course is designed:

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
c) to introduce students to pros and cons of scripting vs. compiled programming languages.

Python is an interpreted, inherently object oriented dynamic language which has been gaining popularity for the past several years. It’s widely used by organizations such as NASA, Google and Industrial Light and Magic, among others.

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.

BS2

Graph Theory

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.

BS2

Data Structures

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.

BS3

Advanced Algorithms

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.

BS3

Inferential Statistics

BS3

Regression models

Passer site announcements

Site announcements

Everyone enrolled in one of my courses has a personal profile page in my Moodle page. This page allows you to post your picture, a personal statement, and other information you would like your peers, students, or classmates to see. It is visible to all other participants in each course your are enrolled in.

Adding a profile picture

1. In the upper right hand corner click on Your Name, Profile.

2. Go to the User details block. Click on Edit Profile.

3. Scroll to the section labeled User picture.

Click the Add button, (it looks like a piece of paper). This will take you to "file picker" where you can upload a file or you can drag and drop a photo from your computer. NOTE: The picture must be in JPG or PNG format. Moodle will crop the image to a 100x100 pixel square. Your Moodle upload limit applies.

Profile picture

4. Once selected, go to the bottom of the page and click Update profile at the bottom of the page.

NOTE: Refresh your browser if the profile image hasn't changed.

Updating other fields

There are numerous preference settings you can adjust.
You may wish to include some of the following information:

- - Web page address
  - IDs for common peer-to-peer and social networking sites
  - Department
  - Phone numbers
  - Campus Address
  - Click the Update profile button at the bottom of the page.

Other information that cannot be updated

Much of the information in the top section of the Edit profile page is drawn from your LU team account. Changes made to this information will not be retained.

- - Username
  - Password
  - First name
  - Last name
  - Email address — this should always be your campus email address
  - ID number field

If you find any of these are incorrect, please contact me to have it corrected.

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Repetition and perseverance is the key to learning and success !!