DS208BKK
Intro to Machine Learning
Faculty
Anna Rudenko
Research Intern at Skoltech, Moscow
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
Machine learning is the foundation for the technologies that power everything from spam filters to self-driving cars. In this introductory course, students will learn the basics of machine learning, from classical ML to neural networks. Students will also learn how to apply machine learning to real-world problems.
Throughout the course, students will delve into a wide range of machine learning algorithms. They will learn how to preprocess and transform data, select relevant features, handle missing values, and perform model evaluation and validation. Practical exercises and hands-on projects will provide ample opportunities for participants to apply their knowledge and develop their own machine learning models.
By the end of this course, students will be equipped with the necessary skills to embark on their own machine learning projects and make meaningful contributions in this rapidly evolving field. They will understand where they should pay attention when building ML systems.
This abstract was created by LLM (large language model). Want to know more? Join this course.
Learning highlights
- This course’s main objective is to introduce students to the basic elements of modern machine learning, including theoretical foundations and practical applications.
Course outline
15 classes
Session 1
Machine learning applications. Linear algebra basics. KNN.
Session 2
Linear algebra overview. Linear models. Regularization.
Session 3
Convexity. Differentiation. Gradient descent. Stochastic gradient descent.
Session 4
Statistics overview.
Session 5
Principal component analysis.
Session 6
Non-gradient optimization. Greedy search. Decision trees.
Session 7
Bootstrap. Explanation of bagging. Random Forest. Q&A.
Session 8
Gradient boosting.
Session 9
Midterm. Q&A.
Session 10
Introduction to deep learning.
Session 11
Optimization in machine learning. Autograd/ + NN regularization.
Session 12
Convolutions. CNN.
Session 13
Random Processes (HMM). Language modeling.
Session 14
Attention mechanism. Transformers.
Session 15
Generalization of all. Final exam.
Course materials
Books
Media
Prerequisites
Object-oriented programming in Python
Probability theory (basic)
Linear algebra and calculus (basic)
Statistics (basic)
Algorithms and data structures (basic)
Methodology
Our sessions consist of two parts: a lecture session with slides and theoretical materials, followed by a practice session devoted to the discussed topic. The practice sessions will include programming tasks and interactive problem-solving based on real-life examples. Throughout the course, multiple home assignments will enable students to get hands-on experience in implementing machine learning pipelines.
Grading
Anya is currently pursuing her PhD at Skoltech with the focus on efficient numerical methods for Large Language Models (LLMs). She completed her undergraduate studies at ITMO University with a specialization in Mathematical Modelling and in Skoltech with specialization in Data Science. She has extensive pedagogic experience. As teaching assistant she served on courses on optimization methods, numerical linear algebra and recommender systems.
See full profileApply for this course
Intro to Machine Learning
by Anna Rudenko
Total hours
45 Hours
Dates
Jan 29 - Feb 16, 2024
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.