CS-C3240 - Machine Learning D, Lecture, 10.1.2022-8.4.2022

Topic outline

• Lectures
  	Topic	Section1	Supplement
  Week 1
  Mon. 10.01. 14.15-16.00	Data, Model and Loss	2.1-2.3	Slides, Playlist
  Wed. 12.01. 16.15-18.00	Empirical Risk Minimization	3.1-3.2	Slides, Playlist
  Week 2
  Mon. 17.01. 14.15–16.00	Classification I (Logistic regression, SVM, Perceptron)	3.6; 3.7	Slides, Playlist
  Wed. 19.01. 16.15-18.00	Classification II (Applications)		Slides
  Week 3
  Mon. 24.01. 14.15-16.00	Model Validation and selection	6.1-6.3	Slides, Old Lecture
  Wed. 26.01. 16.15–18.00	Project Info/Diagnosing ML	6.6	Notebook, Data, Slides
  Week 4
  Mon. 31.01. 14.15–16.00	Regularization, ML at Helsinki (slides)	7.1-7.3	Slides , Old Lecture
  Wed. 02.02. 16.15-18.00	Deep Learning	3.11	Slides
  Week 5
  Mon. 07.02. 14.15-16.00	Non-Parametric Methods	3.10, 4.4	Slides
  Wed. 09.02. 16.15-18.00	Clustering	8.1-8.3	Slides Playlist
  Week 6
  Mon. 14.02. 14.15-16.00	Feature Engineering	6.6, 9.1-9.2	Slides
  Wed. 16.02. 16.15-18.00	Recap and QnA
  Week 7	(no lectures due to Aalto exam week)
  Week 8
  Mo. 28.02 14.15-16.00	Recap Lecture in U157/U2 Otakaari 1
  Wed. 02.03 16.15-18.00	Recap Lecture in U157/U2 Otakaari 1
  Week 9
  Mo. 07.03 14.15-16.00	Ethical ML in U157/U2 Otakaari 1		slides
  Wed. 09.03 16.15-18.00	QnA for Stage 2 of ML Project

  
  

  1) A. Jung, "Machine Learning: The Basics", http://mlbook.cs.aalto.fi