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Course10610524740 lectures

Foundations and Applications of Machine Learning (Bengali)

Prof. Adway Mitra · IIT Kharagpur

Start with lecture 1 At NPTEL

About

The course will cover basic concepts of Machine Learning - supervised and unsupervised learning, along with algorithms like K-nearest neighbors, Decision Trees, Perceptrons, Neural Networks, Linear Regression, K-means, Hierarchical Clustering etc. Each algorithm will be motivated by possible applications in different domains, especially text and images. The aim will be to convey the ideas and intuitions of the algorithms, avoiding mathematical details. The medium of instruction will be in Bengali.

Intended audience: Undergraduate students, teachers and graduate job aspirants from West Bengal

Prerequisites: Mathematics of 10+2 or college level

Lectures

Week 1

  1. Lecture 01 : AIML চারপাশের নানা উদাহরণ YouTube
  2. Lecture 02 : AI/ML এর উপকরণ YouTube
  3. Lecture 03 : SUPERVISED AND UNSUPERVISED LEARNING YouTube
  4. Lecture 04 : ML Model ও Algorithm YouTube
  5. Lecture 05 : AI /ML problem -এর গঠন YouTube

Week 2

  1. Lecture 6 : K-nearest-neighbor classification/regression YouTube
  2. Lecture 7 : নৈপুণ্যের পরিমাপ : Accuracy, Precision, Recall, Confusion YouTube
  3. Lecture 8 : Discriminative Feature Selection YouTube
  4. Lecture 9 : Decision Tree Algorithm/সিদ্ধান্তলতিকা YouTube
  5. Lecture 10 : classifier -এর সমষ্টি ও Random Forests YouTube

Week 3

  1. Lecture 11 : Probability Theory ফিরে দেখা YouTube
  2. Lecture 12 : Bayesian এবং Naïve Bayes Classifier YouTube
  3. Lecture 13 : Linear Algebra ফিরে দেখা YouTube
  4. Lecture 14 : Linear Classifiers এবং Perceptron Algorithm YouTube
  5. Lecture 15 : Multi-class Linear Classifier এবং Logistic Regression YouTube

Week 4

  1. Lecture 16 : Optimization ফিরে দেখা YouTube
  2. Lecture 17 : Linear (সরল) ও Regularized (পরিশোধিত) Regression YouTube
  3. Lecture 18 : Max-margin Linear Classification/সর্বোচ্চ ব্যবধানের শ্রেণীবিভাজন YouTube
  4. Lecture 19 : কৃত্রিম স্নায়ুতন্ত্র/Basic Neural Networks YouTube
  5. Lecture 20 : Neural Network গঠন ও Backpropagation YouTube

Week 5

  1. Lecture 21 : Overfitting and Underfitting YouTube
  2. Lecture 22 : Boosting-এর মাধ্যমে সমষ্টিবদ্ধ অনুমান YouTube
  3. Lecture 23 : Data র মাত্রা (dimensionality) নিয়ন্ত্রণ YouTube
  4. Lecture 24 : শ্রেণী ও বৈশিষ্টের মানের অসাম্য YouTube
  5. Lecture 25 : Supervised Learning অভিযান YouTube

Week 6

  1. Lecture 26 : Hierarchical Clustering/স্তরভিত্তিক গোষ্ঠীকরণ YouTube
  2. Lecture 27 : K-means Clustering/ K-গড় গোষ্ঠীকরণ YouTube
  3. Lecture 28 : Evaluation of Clustering/গোষ্ঠীকরণের মূল্যায়ন YouTube
  4. Lecture 29 : Mean-shift এবং DB-Scan গোষ্ঠীকরণ YouTube
  5. Lecture 30 : Graph-based Clustering/গোষ্ঠীকরণ YouTube

Week 7

  1. Lecture 31 : Time-series/সময়ক্রমের বিশ্লেষণ YouTube
  2. Lecture 32 : ব্যতিক্রমী উদাহরণ চিহ্নিতকরণ YouTube
  3. Lecture 33 : Image/চিত্র বিশ্লেষণ YouTube
  4. Lecture 34 : Neural Features for Images YouTube
  5. Lecture 35 : লিখিত Data ও ভাষা বিশ্লেষণ YouTube

Week 8

  1. Lecture 36 : Sequential Neural Models and Natural Language Processing YouTube
  2. Lecture 37 : সৃষ্টিমূলক/Generative Models, Reinforcement Learning YouTube
  3. Lecture 38 :Transfer Learning and Domain Adaptation YouTube
  4. Lecture 39 : নীতি, নিরপেক্ষতা ও বোধগম্যতা YouTube
  5. Lecture 40 : Machine Learning for Climate Sciences YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kharagpur
Department
Computer Science and Engineering
Teacher
Prof. Adway Mitra
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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