Program Overview

Machine Learning Course

The Machine Learning course is a basic introduction to machine learning, providing participants with knowledge of a framework for data modeling, fundamental and widely applied machine learning methods, and Python as a tool for data analysis, data modeling, and machine learning.

Overall Course Objectives

The course objectives include:

• Providing a framework for data modeling
• Introducing fundamental and widely applied machine learning methods
• Utilizing Python for data analysis, data modeling, and machine learning
• Enabling participants to apply machine learning for modeling real-world data

Learning Objectives

The learning objectives of the course are:

• Explaining the major steps involved in data modeling
• Discussing key machine learning concepts
• Matching practical problems to standard data modeling problems
• Sketching how relevant machine learning methods work and describing their assumptions, strengths, and limitations
• Applying and modifying machine learning algorithms in Python
• Applying visualization techniques and statistics to evaluate model performance
• Selecting, combining, and modifying data modeling tools for data analysis and result dissemination
• Applying the data modeling framework to various application domains

Course Content

The course content includes:

• Structured data modeling
• Data preprocessing and feature extraction
• Summary statistics
• Similarity measures
• Cost functions, including maximum likelihood
• Optimization methods for machine learning
• Overfitting, generalization, regularization, and bias-variance tradeoffs
• Cross-validation
• Statistical evaluation and comparison of machine learning methods
• Visualization and interpretation of models
• Dimensionality reduction
• Classification methods
• Regression methods
• Clustering
• Density estimation
• Anomaly/outlier detection

Possible Start Times

The course starts at week 36, on Tuesdays from 13-17.

Recommended Prerequisites

The recommended prerequisites include:

• Basic course in linear algebra and calculus
• Basic knowledge of probability theory and statistics
• Basic knowledge of Python
• Specific course codes: 01001/01002/01003/01004/01005/02402/02403/02002/02101/02102/02525/02631/02632/02633/02692

Teaching Method

The teaching method alternates between lectures, problem classes, and hands-on Python exercises.

Faculty

The faculty includes:

• Morten Mřrup
• Georgios Arvanitidis
• Bjřrn Sand Jensen

Remarks

The course is a basic machine learning course relevant for all master programs, designed as a stand-alone course providing an introduction to basic machine learning, the mathematics behind the methods, and hands-on experience in their use.

Registration

• Language: English
• Duration: 13 weeks
• Institute: Compute
• Place: DTU Lyngby Campus
• Course code: 02452
• Course type: Candidate
• Semester start: Week 36
• Semester end: Week 49
• Days: Tuesdays 13-17
• Price: 9,250.00 DKK

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