Study Guide

Objectives (course unit)

After completing this course, the student
-can compute and understands basic statistical measures
-is able to use basic statistical methods
-is able to conduct hypothesis testing
-knows the basic principles of machine learning
-knows the concepts of regression, clustering and classification
-knows the concepts of supervised and unsupervised learning
-is able to utilize statistical methods in technical problem solving

Content (course unit)

Statistical charts and numbers, probability, regression and correlation, hypothesis testing.
Basic concepts of machine learning

Prerequisites (course unit)

Student needs to have basic skills in using Microsoft Excel or some other similar kind of software.

Assessment criteria, satisfactory (1-2) (course unit)

Student understands and is able to name and define the basic concepts of statistics and machine learning.
Student manages the assigned tasks under supervision and knows different ways to conduct the statistical analyses, but cannot justify his/her choices. Student's way to use the statistical analyses is based on routine and pre-learned performance.
Student can give and receive feedback, and is able to consider and assess things from his/her viewpoint. Student takes responsibility for his/her own work.

Assessment criteria, good (3-4) (course unit)

Student is able to structure relations between the basic concepts and is able to apply, explain and compare different statistical and machine learning methods.
Student can select the most appropriate course of action from diverse options and justify his/her choice. Student is able to apply the advanced concepts when solving technical problems.
Student can give and receive feedback actively and constructively, and considers and assesses things both from his/her and the close community's viewpoint. Student

Assessment criteria, excellent (5) (course unit)

Student is able to understand extensive entities and relation between them. Student is able to generalize, analyse and related the advanced problems to the professional context.
Student can search for diverse courses of action and solution alternatives, justify his/her choices and try new courses of action. Student assesses diverse solution alternatives creatively. Student has skills to present and justify the chosen methods when solving problems in a logical way.
Student uses feedback systematically as a professional growth tool in his/her own work and the community. Student can cooperate responsibly, flexibly and constructively and works in a committed manner.

Location and time

Dates and times are shown in TuniMoodle and in Intranet.

Exam schedules

Three exams:

The first exam, part 1, will be held on Monday, 7th of October at 10.15-12.00 in D1-04 and

The second exam, part 2, will be held on Wednesday, 20th of November 2024 at 09.15-12.00 in D1-04 (auditorium).

The third exam part 3, will be held on Wednesday, 11th of December 2024 at 08.15 - 11.00 in the classroom B2-25.

Two re-sit exams, the first one will be held on Friday, 17th of January 2025 at 13.15-16.00 in the classroom B4-18 & B4-27, and the second on Friday, 7th of February at 13.15-16.00 in Festival Hall D1-04.

Assessment methods and criteria

The final grade is based on the exam and the homework. A homework package is given weekly (appr. 8+5 packages). One point is given for every submitted homework package in Moodle. Homework packages are not accepted by email. The maximum points for the first and second test (Statistics) is 21 + 21 = 42 points and the third test (Machine Learning) 28 points. The homework and the test together give the maximum of 83 points. The grade is based on the following table

21 points -> grade 1
33,5 points -> grade 2
46 points -> grade 3
58,5 points -> grade 4
71 points -> grade 5

Assessment scale

0-5

Teaching methods

Contact studies, individual work, homework, videos

Learning materials

All material, theory and exercises, can be found in TuniMoodle. If necessary, a student can use math books he/she has used before and the Internet to search more information about the topics. Some solutions for the exercises will be published in TuniMoodle after every deadline.

Content scheduling

Topics and dates are shown in TuniMoodle.

Completion alternatives

-

Practical training and working life cooperation

-

International connections

-

Further information

It is recommended that a student has a calculator, a computer and a formula book.

Objectives (course unit)

After completing this course, the student
-can compute and understands basic statistical measures
-is able to use basic statistical methods
-is able to conduct hypothesis testing
-knows the basic principles of machine learning
-knows the concepts of regression, clustering and classification
-knows the concepts of supervised and unsupervised learning
-is able to utilize statistical methods in technical problem solving

Content (course unit)

Statistical charts and numbers, probability, regression and correlation, hypothesis testing.
Basic concepts of machine learning

Prerequisites (course unit)

Student needs to have basic skills in using Microsoft Excel or some other similar kind of software.

Assessment criteria, satisfactory (1-2) (course unit)

Student understands and is able to name and define the basic concepts of statistics and machine learning.
Student manages the assigned tasks under supervision and knows different ways to conduct the statistical analyses, but cannot justify his/her choices. Student's way to use the statistical analyses is based on routine and pre-learned performance.
Student can give and receive feedback, and is able to consider and assess things from his/her viewpoint. Student takes responsibility for his/her own work.

Assessment criteria, good (3-4) (course unit)

Student is able to structure relations between the basic concepts and is able to apply, explain and compare different statistical and machine learning methods.
Student can select the most appropriate course of action from diverse options and justify his/her choice. Student is able to apply the advanced concepts when solving technical problems.
Student can give and receive feedback actively and constructively, and considers and assesses things both from his/her and the close community's viewpoint. Student

Assessment criteria, excellent (5) (course unit)

Student is able to understand extensive entities and relation between them. Student is able to generalize, analyse and related the advanced problems to the professional context.
Student can search for diverse courses of action and solution alternatives, justify his/her choices and try new courses of action. Student assesses diverse solution alternatives creatively. Student has skills to present and justify the chosen methods when solving problems in a logical way.
Student uses feedback systematically as a professional growth tool in his/her own work and the community. Student can cooperate responsibly, flexibly and constructively and works in a committed manner.

Location and time

Period 1: on Thursdays between 14-17 in room B2-35
Period 2: on Fridays between 9-12 in room B2-35

Exam schedules

3 exams during the course. Scheduling to be informed in Moodle.

Assessment methods and criteria

Course grading is based on the following evaluation areas
Course activity (30%)
Returned assignments (30%)
Exams (40%)

Grade thresholds are determined from the points collected from the evaluation areas as a relative percentage of course max points (50p) as follows:
35%: 1
50%: 2
65%: 3
80%: 4
90%: 5

Assessment scale

0-5

Teaching methods

Chosen from the following based on teacher's pedagogical evaluation: Contact teaching, Remote teaching, Independent learning, Lesson excercises, Homework, Problem-based learning, Collaborative learninng, Group work, Excercise assignments, Question-based teaching, Question-based learning, PC-excercises, Exams

Learning materials

Provided in Moodle

Student workload

Lessons ca 30h
Exams ca 10 h
Group studying ca 45 h
Independent studying ca 50 h

Content scheduling

Period 1
-Use of software in statistics
-Basic statistical measures & methods
-Hypothesis testing

Period 2
-Principles of machine learning
-Regression, clustering and classification
-Supervised and unsupervised learning

Completion alternatives

To be negotiated with teacher. Teacher is not obliged to grant an alternative way of execution.