ML2022

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Learning Algorithms- Spring 2022

Instructors: Professor Maziar Salahi and Assis.Professor Sadegh Eskandari

Lectures: Sunday 13:15-14:45 and Thursday 13:15-14:45

Online communication: (Videos and Homeworks) https://ecent2.guilan.ac.ir/

Textbooks: Pattern Recognition and Machine Learning by C. Bishop, and Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by A. Géron

Description: This course aims to teach the fundamentals of machine learning. We will take an in-depth look at probability theory, Bayesian probability, parametric and non-parametric density estimation, linear regression and classification, neural networds, sparse kernel methods and decision trees. We will also provide hands-on lectures using Scikit-Learn.

Detailed syllabus

Week 1

• Introduction to Machine Learning (Slides)
• Reading assignment for next week: Advanced Programming using Python Course or other similar Python programming courses

Week 2

• Introduction to Probability Theory (Slides)
• Reading assignment for next week: Chapter1: Introduction from the book “PRML”

Week 3

• Probability Distributions and Parametric Density Estimation (Slides)
• Reading assignment for next week: Chapter2: Probability Distributions sections 2.1 to 2.4 from the book “PRML”

Week 4

• Non-Parametric Density Estimation (Slides)
• Reading assignment for next week: Chapter2: Probability Distributions section 2.5 from the book “PRML”

Week 5

• Linear Models for Regression (Slides) (Hands-on basis funtions) (Hands-on polynomial curve fitting) (Hands-on California Housing Prediction)
• Reading assignment for next week: Chapter3: Linear Models for Regression from the book “PRML”, Chapter2: End-to-End Machine Learning Project from the book “Hands-on”