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CE40477: Machine learning

Spring 2025, Group 2; Computer Engineering Department, Sharif University of Technology, Tehran, Tehran Province, Iran

Classes: Sundays and Tuesdays, 10:30-12:00, Amouzesh - Class 310

This course provides a comprehensive introduction to machine learning, covering fundamental concepts, algorithms, and applications. Topics include supervised and unsupervised learning, optimization techniques, probabilistic models, deep learning, and reinforcement learning. Students will gain hands-on experience through practical assignments and projects, applying ML techniques to real-world problems. The course is designed for students with a background in linear algebra, probability, and programming.

Syllabus    Calendar


Instructors

Hamid R. Rabiee

Distinguished Professor, Computer Engineering Dept.

Office CE804, CE803, and CE802
Tel +98 21 6606 9143, +98 21 6616 6683

rabiee@sharif.edu

Zahra Dehghanian

PhD Student, Guest Lecturer

Office CE802


zahra.dehghanian97@sharif.edu

Teaching Assistants

Mahta Fetrat
Lead TA

m.fetrat@sharif.edu

Mahdi Mastani
Assistant lead TA

mastanimahdi1@gmail.com

Asal Meskin
TA

asalmskin82@gmail.com

Sajjad Rahmani
TA

Saeide Danaei
TA

Amir Vahedi
TA

MohammadReza Davoodi
TA

mohammadreza1376@gmail.com

Mehran Advand
TA

Samin Kakaei
TA

samin.kakaei@gmail.com

Arshia Gharooni
TA

arshiyagharoony@gmail.com

Alireza Mirrokni
TA

Ali Najjar
TA

anajar13750@gmail.com

Kiarash Kiani
TA

kiarashkia138@gmail.com

Amin Fadaei
TA

Ali HajiSadeghian
TA

alihajisadeghian250@gmail.com

Erfan Jafari
TA

MohammadHossein Mahdikhani
TA

Alireza Haghshenas
TA

Danial Gharibi
TA

danial.mail.gh@gmail.com

Mahan Beyhaghi
TA

Arshia Dadras
TA

arshiadadras1234@gmail.com

Shayan Aryana
TA

Shayanaryania@gmail.com

AmirReza Azari
TA

Mahdi Aghaei
TA

mahdiaghaei.official@gmail.com

Mohammad Mahdi Farhadi
TA

mohammadmahdifarhadi93@gmail.com

Mohammad Aref Zarezadeh
TA

arefzarezade@gmail.com

Radin Shahdaei
TA

Radin.shahdaei@gmail.com

Shayan Baghaei nejad
TA

Reza Ghorbani
TA

Zahra Asgari
TA


Materials

Prerequisite

Linear Algebra Probability and Statistics

These are the prerequisites for this course. Only the first two slides of Probability and Statistics are required.

Session 1

slide

Session 2

slide

Prerequisite workshop
Esfand 2, 1403

Linear Algebra Probability and Statistics Workshop summary Workshop recording

Session 3

slide

Session 4

slide

Session 5

slide

Session 6

slide

Session 7

slide

Session 8

slide

Assignments

Regression, Bias and Variance, ML/MAP
HW1

Release Date: 1403/11/30, Submission Deadline: 1403/12/10, late submission until 1403/12/13 at 23:59

Homework Document Notebook Supplementary Problems
Homework 2
HW2

Release Date: 1403/12/16, Submission Deadline: 1403/12/26

Homework 3
HW3

Release Date: 1404/01/05, Submission Deadline: 1404/01/19

Homework 4
HW4

Release Date: 1404/01/24, Submission Deadline: 1404/02/07

Homework 5
HW5

Release Date: 1404/02/14, Submission Deadline: 1404/02/28

Homework 6
HW6

Release Date: 1404/03/04, Submission Deadline: 1404/03/20

News

Workshop on Linear Algebra & Probability
1403-12-02

A workshop to review the essential prerequisites for Machine Learning, covering Linear Algebra and Probability & Statistics, will be held on Thursday at 9 AM via the provided link. The session will be conducted by Mahdi Aghaei.

link
Make-up Session on Wednesday
1403-12-08

A make-up session will be held on Wednesday from 1:30 to 3:00 PM.

ML-Talk Sessions on Tuesdays

ML-Talk sessions will take place on Tuesdays to be announced, from 12:00 to 1:00 PM. The specific dates will be shared later.

Problem sets

No Problem sets has been posted yet.