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This repository contains all the sessions covered during the IIIT-H AI ML course.

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Talent Sprint

https://iiith-aiml.talentsprint.com/dashboard

Modules

  1. Python Essentials for AI/ML

    • Intro to Python
    • String, list & for Loop
    • List Comprehensions & files
    • Indentation & Code Blocking

  2. Maths Essentials for AI/ML

    1. Linear Algebra
    2. Calculus
    3. Statistics
      • Measures of Center
      • Measures of Spread
    4. Probability
      • Addition Rule
      • Multiplication Rule
      • Conditional Probability

  3. M0: Getting Ready

    1. Terminology and Definitions
      • What is Machine Learning?
      • Question: How are Artificial Intelligence, Machine Learning and other things related?
      • Question: Where does Data Science fit in all this? Are different skills required for it?
      • Visuals
      • ML Glossary - https://developers.google.com/machine-learning/glossary
    2. Fundamental Abstraction
      • Problem Space
      • ML Frameworks
      • Spam Detection
      • Training
      • Testing
    3. ML Avatars
      • The Machine Learning Framework
      • Spam Detection
      • Medical Diagnosis
      • Stock Trading
      • Sentiment Analysis
      • Disease Confirmation
      • Product Recommendation
      • Loan Approval
      • Face Recognition
      • Voice Detection

  4. M1: Problem Formulation and Solving

    • Formulating Real World Problems for AI/ML
    • Classification and Regression Problems
    • Intuitive and Simple Algorithms
    • Representation of the World and Real Data
    • Visualization, Data Preparation, Unsupervided Learning
    • End-to-End Problem Solving

  5. M2: Closer Look at AI/ML Algorithms

    • Linear Algorithms, Optimization and Training
    • Non-Linear Solutions and MLP
    • Gradient Descent and Backpropagation
    • Decision Trees, Random Forest, and Ensembles
    • Principles and Practice of ML
    • Support Vector Machines and Kernels

  6. M3: Deep Learning and Practical Issues

    • Introduction of Deep Learning and Toolchain
    • Convolutional Neural Networks
    • Auto-Encoders
    • Recurrent Neural Networks
    • Overview of Advance Topics
    • Human in the Loop Solutions, Deployment

Program Coverage

Date FirstHalf SecondHalf
Saturday,02-Mar-19 Python / Math Sessions Python / Math Sessions
Sunday,03-Mar-19 Python / Math Sessions Python / Math Sessions
Saturday,09-Mar-19 Class Test; Pandas and Mathplotlib; Exp_A_Data Munging Cloud API(Exp); ML Avatars
Sunday,10-Mar-19 Class test; Experiment 1,2,3; 3 ML Algorithms Decision Trees and Over fitting; KNN & Linar Classfier (exp);
Saturday,16-Mar-19 Lecture - Linear Regression Mini_Hackathon
Sunday,17-Mar-19 Lecture Day 1.Representing Text and Language, 2.Perceptrons, Neural Networks, Gradient Descent Lecture 3.Learning Representations: Word2Vec, Lecture 4.Dimensionality Reduction
Saturday,23-Mar-19 Individual Lab : Expt 1: Newsgroups: Bow; Expt 2: Newsgroups: nGrams;
Sunday,24-Mar-19 Class Test Demo Lec 1: Word2Vec: Mahabharatha; Ind. Lec 1: Performance Metrics Expt 5: Newsgroups: Word2Vec; Expt 6: Linear Classifier; Expt 7: Applying other metrics for Newsgroups
Saturday,30-Mar-19 Mini-Hackathon Mini-Hackathon
Sunday,31-Mar-19 Lecture: PCA and EigenFaces, Multi-Layer Perceptrons Features for Perception-1, Features for Perception-2
Saturday,06-Apr-19 Festival week Holiday. Happy Ugadi Festival week Holiday. Happy Ugadi
Sunday,07-Apr-19 Festival week Holiday. Happy Ugadi Festival week Holiday. Happy Ugadi
Saturday,13-Apr-19 "Individual Lab : Expt 8: MNIST-MLP Expt 9: Celebrity Faces - PCA Expt 10: CIFAR-100 Expt 11: Eigenfaces for classification Expt 12: Speech-""Yes""/""No"" Classifier"
Sunday,14-Apr-19 Class Test Ind. Lec 2: Visualization; Demo Lec 2: Cloud APIs "Individual Lab : Expt 13: Visualization - TSNE Expt 14: Visualization - ISOMAP Expt 15: Alexa API Experiments"
Saturday,20-Apr-19 Hackathon Hackathon
Sunday,21-Apr-19 09. Convolutional Layer 10. Back Propagation 11. ML Pipeline 12. Overfitting and Generalization
Saturday,27-Apr-19 Expt 16: Leave one out Validation Expt 17: K-fold Validation Expt 18: Polynomial Curve Fitting
Sunday,28-Apr-19 Class Test Ind. Lec 3: Clustering Demo Lec 3: PyTorch Expt 19: K-Means Expt 20: Hierarchical Clustering Expt 21: Fashion MNIST; Expt 22: Polynomial curve fitting Expt 23: Instrumenting CNN
Saturday,04-May-19 Mini-Hackathon Mini-Hackathon
Sunday,05-May-19 Class Test; 13. Random Forests, Ensemble Techniques 14. Support Vector Machines 15. Time Series/RNN 16. Human in the Loop Systems
Saturday,11-May-19 Expt 24: SVM, SVM with kernels Expt 25: Face recognition with SVM Expt 26: Random Forests, Ensemble Methods Expt 27: Weather Prediction Expt 28: Rocchio's algorithm
Sunday,12-May-19 Class Test Ind. Lec 4: Recommendation Systems Demo Lec 4: Timeseries Application Ind. Lec 5: Deployment, Practical Issues; Expt 29: Movie Recommendation system KNN Expt 30: Movie Recommendation system SVD KNN Expt 31: Alexa Chatbot
Saturday,18-May-19 Hackathon Hackathon
Sunday,19-May-19 Lecture Sessions : Class Test; 17. Convolutional Neural Networks; 18. Autoencoders Lexture Sessions : 19. Appreciating CNNs; 20. RNN, LSTM, GRU
Saturday,25-May-19 Expt 32: Transfer learning and Finetuning Expt 33: Visualization of CNNs
Sunday,26-May-19 Class Test Lecture 5: Model Compression; Demo: Deployment, Practical Issues "Expt 37: Uniform and Non Uniform Quantizations; Expt 38: Student and Teacher Networks; Expt 39: Weight Intializations and updates"
Saturday,01-Jun-19 Mini-Hackathon Mini-Hackathon
Sunday,02-Jun-19 Class Test 21. Beyond AlexNet 22. BP Revisited 23.Siamese Networks 24. Advanced Topics: GANs
Saturday,08-Jun-19 Expt 40: Siamese Expt 41: GAN Tutorial from PyTorch Expt 42: Tuning Hyperparameter learning rate Expt 43: Tuning hyperparamter optimizer _ Adam Expt 44: Hackathon debrief
Sunday,09-Jun-19 Hackathon Hackathon

GRADES

https://iiith-talentsprint.trainmoo.in/applite/grades

Scores 602/667 90.25%

Name Score Outof Percentage
M0_W0_CT_2 - 03/03/2019 9 9 100.00%
M0_W0_CT_1 - 03/03/2019 6 6 100.00%
M0_W1_CT_ - 03/09/2019 9 9 100.00%
M0_W1_CT_ - 03/10/2019 6 6 100.00%
M0_W1_IL_1 - 03/16/2019 6 10 60.00%
M0_W0_MH_1 - 03/16/2019 22 25 88.00%
M1_W1_CT_2 - 03/17/2019 9 9 100.00%
M1_W1_CT_1 - 03/17/2019 6 6 100.00%
M1_W1_WT_1 - 03/23/2019 10 10 100.00%
M1_W2_CT_1 - 03/24/2019 6 6 100.00%
M1_W2_CT_2 - 03/24/2019 9 9 100.00%
M1_W2_WT_2 - 03/28/2019 10 10 100.00%
M1_W1_IL_2 - 03/30/2019 6 8 75.00%
M1_W3_MH_2 - 03/30/2019 24 25 96.00%
M1_W3_CT_2 - 03/31/2019 8 9 88.89%
M1_W3_CT_1 - 03/31/2019 6 6 100.00%
M1_W3_WT_3 - 04/04/2019 10 10 100.00%
M1_W2_IL_3 - 04/06/2019 6 6 100.00%
M1_W3_IL_4 - 04/13/2019 10 10 100.00%
M1_W4_CT_1 - 04/14/2019 6 6 100.00%
M1_W4_CT_2 - 04/14/2019 9 9 100.00%
M1_W4_WT_4 - 04/18/2019 10 10 100.00%
M1_W4_IL_5 - 04/21/2019 2 6 33.33%
M1_W4_H_1 - 04/21/2019 49 50 98.00%
M2_W5_CT_2 - 04/21/2019 4 9 44.44%
M2_W5_CT_1 - 04/21/2019 6 6 100.00%
M2_W5_WT_5 - 04/25/2019 10 10 100.00%
M2_W1_IL_6 - 04/28/2019 6 6 100.00%
M2_W6_CT_2 - 04/28/2019 9 9 100.00%
M2_W6_CT_1 - 04/28/2019 6 6 100.00%
M2_W6_WT_6 - 05/02/2019 10 10 100.00%
M2_W6_MH_3 - 05/04/2019 22 25 88.00%
M2_W7_CT_1 - 05/05/2019 6 6 100.00%
M2_W7_CT_2 - 05/05/2019 9 9 100.00%
M2_W2_IL_7 - 05/05/2019 10 10 100.00%
M2_W7_WT_7 - 05/09/2019 10 10 100.00%
M2_W3_IL_8 - 05/11/2019 10 10 100.00%
M2_W8_CT_1 - 05/12/2019 6 6 100.00%
M2_W8_CT_2 - 05/12/2019 7 9 77.78%
M2_W8_WT_8 - 05/16/2019 10 10 100.00%
M2_W4_IL_9 - 05/18/2019 4 4 100.00%
M2_W8_H_2 - 05/18/2019 36 50 72.00%
M3_W9_CT - 05/19/2019 4 6 66.67%
M3_W9_WT_9 - 05/23/2019 10 10 100.00%
M3_W1_IL_10 - 05/25/2019 12 15 80.00%
M3_W10_CT_1 - 05/26/2019 6 6 100.00%
M3_W10_CT_2 - 05/26/2019 9 9 100.00%
M3_W10_WT_10 - 05/30/2019 10 10 100.00%
M3_W10_MH_4 - 06/01/2019 27 25 108.00%
M3_W2_IL_11 - 06/01/2019 6 9 66.67%
M3_W11_CT_1 - 06/02/2019 6 6 100.00%
M3_W11_CT_2 - 06/02/2019 9 9 100.00%
M3_W11_WT_11 - 06/06/2019 2 10 20.00%
M3_W3_IL_12 - 06/08/2019 12 12 100.00%
M3_W12_CT_1 - 06/09/2019 6 6 100.00%
M3_W11_H_3 - 06/09/2019 39 50 78.00%
M3_W12_CT_2 - 06/09/2019 9 9 100.00%

Certificate

IMG_3602 IMG_3603

Group Picture

AIML BLR B8 Class Photo

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