CourseDiary_CST395 - NEURAL NETWORKS AND DEEP LEARNING.pdf
- 1. Subject CST395 - NEURAL NETWORKS AND DEEP LEARNING Batch CS 2K20 Academic Year 2022-23 Total hours taken 19 Name of Teacher ASJAD NABEEL P Designation Asst. Professor Department Computer Science and Engineering GOVERNMENT COLLEGE OF ENGINEERING KANNUR MANGATTUPARAMBA PARASSINIKKADAVU.P.O KANNUR-670563 PH: 04972780227 COURSE DIARY CST395 - NEURAL NETWORKS AND DEEP LEARNING 1
- 2. Sl.No. Content 1 GENERAL INSTRUCTIONS 2 VISION & MISION OF COLLEGE 3 PROGRAM OUTCOME 4 VISION & MISSION OF DEPARTMENT 5 PROGRAM EDUCATIONAL OBJECTIVES 6 PROGRAM SPECIFIC OUTCOMES 7 COURSE OUTCOMES 8 ACADEMIC CALENDAR 9 TIMETABLE 10 SYLLABUS 11 SUBJECT PLAN 12 SUBJECT COVERAGE 13 ASSIGNMENTS 14 SERIES EXAMS 15 INTERNALMARK 16 COURSE EXIT SURVEY 17 YEAR CALENDAR 18 ATTENDANCE SUMMARY TABLE OF CONTENTS CST395 - NEURAL NETWORKS AND DEEP LEARNING 2
- 3. General Instructions Student's performance should be evaluated solely on an academic basis Student's evaluation should be fair, consistent, transparent and accountable. Evaluation of student's performance should be disclosed to the students. 1. Keep the Course Diary up to date by clearly indicating the subject coverage and students attendance on the relevant pages. 2. Paste the syllabus in the relevant page. 3. Write / paste the Course plan in the relevant page. 4. Events in a semester such as Series Test days, Cultural / Celebration days, days for extra / co-curricular activities etc. may be indicated in the Year Calendar. 5. Assignment details may be written in the Course Diary or may be filed in the Course File. (i) Minimum 2 no. of assignments should be given. (ii) Different sets of questions may be given in an assignment (atleast three) to a class. (iii) Assignments may be in the form of written - closed / open book, individual / group, home assignment, or in the form of oral presentation, quiz, seminar etc. 6. Show complete split up of sessional marks in the page "Particulars of Marks". Final sessional mark for each student should be equal to the sum of marks awarded for Assignments (10) and Series Tests (40). 7. All the entries in the course diary must be, legibly written without overwriting and free of errors. 8. Do not count marks of class tests along with the series test for computing sessional mark. 9. The staff member will be responsible for the safe custody of the Course Diary and (s)he should return it to the HOD at the end of semester or earlier if (s)he leaves the department or discontinue the subject. 10. Follow KTU regulations for computing sessional marks. PRINCIPAL CST395 - NEURAL NETWORKS AND DEEP LEARNING 3
- 4. VISION OF THE COLLEGE "A globally renowned institution of excellence in engineering, education, research and consultancy." MISSION OF THE COLLEGE "To contribute to the society by providing quality education and training, leading to innovation , entrepreneurship and sustainable growth." CST395 - NEURAL NETWORKS AND DEEP LEARNING 4
- 5. Heading Content PO1 Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems PO2 Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. PO3 Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. PO4 Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions PO5 Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations PO6 The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice PO7 Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development PO8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice PO9 Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings PO10 Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions. PO11 Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one‘s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments PO12 Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change PROGRAM OUTCOME CST395 - NEURAL NETWORKS AND DEEP LEARNING 5
- 6. VISION OF COMPUTER SCIENCE AND ENGINEERING DEPARTMENT "A centre of excellence, in the field of Computer Science and Engineering education and research, which extends its appreciated services to the industry and society." MISSION OF COMPUTER SCIENCE AND ENGINEERING DEPARTMENT "To develop engineers with excellent analytic, design and implementation skills, who can expertise themselves as computer professionals, research engineers, entrepreneurs or as managers, while fulfilling their ethical and social responsibilities, in a globally competitive environment." CST395 - NEURAL NETWORKS AND DEEP LEARNING 6
- 7. SlNo. Topic 1 Demonstrate the basic concepts of machine learning models and performance measures. 2 Illustrate the basic concepts of neural networks and its practical issues 3 Outline the standard regularization and optimization techniques for deep neural networks 4 Build CNN and RNN models for different use cases 5 Explain the concepts of modern RNNs like LSTM, GRU COURSE OUTCOME CST395 - NEURAL NETWORKS AND DEEP LEARNING 7
- 8. Heading Content PEO1 Be employed as computer science professionals beyond entry-level positions or be making satisfactory progress in graduate programs. PEO2 Be able to route their talents in to post graduate and research programs, promoting remarkable advancements in emerging areas. PEO3 Have peer-recognized expertise together with the ability to articulate that expertise as computer science professionals. PEO4 Apply good analytic, design, and implementation skills required to formulate and solve computer science problems. PEO5 Demonstrate that they can function, communicate, collaborate and continue to learn effectively as ethically and socially responsible computer science professionals. PROGRAM EDUCATIONAL OBJECTIVES Heading Content PSO1 Foundation of Computer System: Ability to understand the principles and working of computer systems. Students can assess the hardware and software aspects of computer systems. PSO2 Foundations of Software development: Ability to understand the structure and development methodologies of software systems. Possess professional skills and knowledge of software design process. Familiarity and practical competence with a broad range of programming language and open source platforms. PSO3 Foundation of mathematical concepts: Ability to apply mathematical methodologies to solve computation task, model real world problem using appropriate data structure and suitable algorithm. PSO4 Applications of Computing and Research Ability: Ability to use knowledge in various domains to identify research gaps and hence to provide solution to new ideas and innovations. PROGRAM SPECIFIC OUTCOMES CST395 - NEURAL NETWORKS AND DEEP LEARNING 8
- 9. Sep 2022 Oct 2022 Description Description Sat 01 Sun 02 Mon 03 Tue 04 Wed 05 Thu 06 Fri 07 Sat 08 Sun 09 Mon 10 Tue 11 Wed 12 Thu 13 Fri 14 Sat 15 Sun 16 Mon 17 Tue 18 Wed 19 Thu 20 Fri 21 Thu 22 Sat 22 Fri 23 Sun 23 Sat 24 Mon 24 Sun 25 Tue 25 Mon 26 Wed 26 Tue 27 Thu 27 Wed 28 Fri 28 Thu 29 Sat 29 Fri 30 Sun 30 Mon 31 ACADEMIC CALENDAR Days Date Class Days Date Class CST395 - NEURAL NETWORKS AND DEEP LEARNING 9
- 10. Nov 2022 Dec 2022 Description Description Tue 01 Thu 01 Wed 02 Fri 02 Thu 03 Sat 03 Fri 04 Sun 04 Sat 05 Mon 05 Sun 06 Tue 06 Mon 07 Wed 07 Tue 08 Thu 08 Wed 09 Fri 09 Thu 10 Sat 10 Fri 11 Sun 11 Sat 12 Mon 12 Sun 13 Tue 13 Mon 14 Wed 14 Tue 15 Thu 15 Wed 16 Fri 16 Thu 17 Sat 17 Fri 18 Sun 18 Sat 19 Mon 19 Sun 20 Tue 20 Mon 21 Wed 21 Tue 22 Thu 22 Wed 23 Series Exam 1 Fri 23 Thu 24 Sat 24 Fri 25 Sun 25 Sat 26 Mon 26 Sun 27 Tue 27 Mon 28 Wed 28 Tue 29 Thu 29 Wed 30 Fri 30 Sat 31 ACADEMIC CALENDAR Days Date Class Days Date Class CST395 - NEURAL NETWORKS AND DEEP LEARNING 10
- 11. Jan 2023 Description Sun 01 Mon 02 Tue 03 Wed 04 Thu 05 Fri 06 ACADEMIC CALENDAR Days Date Class CST395 - NEURAL NETWORKS AND DEEP LEARNING 11
- 12. Day Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 Period 9 Monday -- -- -- -- -- CST395 - NEURAL NETWORKS AND DEEP LEARNING -- -- -- Tuesday -- -- -- -- -- -- -- -- -- Wednesday -- -- -- -- -- CST395 - NEURAL NETWORKS AND DEEP LEARNING CST395 - NEURAL NETWORKS AND DEEP LEARNING -- -- Thursday -- -- -- -- -- -- -- -- -- Friday -- -- CST395 - NEURAL NETWORKS AND DEEP LEARNING -- -- -- -- -- -- TIMETABLE CST395 - NEURAL NETWORKS AND DEEP LEARNING 12
- 13. Module Topic 1 1.1. Basics of Machine Learning 1.1.1. Machine Learning basics - Learning algorithms - Supervised, Unsupervised, Reinforcement, Overfitting, Underfitting, Hyper parameters and Validation sets 1.1.2. Estimators -Bias and Variance. Challenges in machine learning 1.1.3. Simple Linear Regression, Logistic Regression 1.1.4. Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 2 2.1. Neural Networks 2.1.1. Introduction to neural networks 2.1.2. Single layer perceptrons, Multi Layer Perceptrons (MLPs), Representation Power of MLPs 2.1.3. Activation functions - Sigmoid, Tanh, ReLU, Softmax 2.1.4. Risk minimization, Loss function 2.1.5. Training MLPs with backpropagation 2.1.6. Practical issues in neural network training - The Problem of Overfitting, Vanishing and exploding gradient problems 2.1.7. Difficulties in convergence, Local and spurious Optima, Computational Challenges 2.1.8. Applications of neural networks 3 3.1. Deep learning 3.1.1. Introduction to deep learning, Deep feed forward network, Training deep models 3.1.2. Optimization techniques - Gradient Descent (GD), GD with momentum 3.1.3. Nesterov accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam 3.1.4. Regularization Techniques - L1 and L2 regularization, Early stopping, Dataset augmentation 3.1.5. Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout, Parameter initialization 4 4.1. Convolutional Neural Network 4.1.1. Convolutional Neural Networks – Convolution operation, Motivation, Pooling, Convolution and Pooling as an infinitely strong prior 4.1.2. Variants of convolution functions, Structured outputs, Data types 4.1.3. Efficient convolution algorithms 4.1.4. Practical use cases for CNNs 4.1.5. Case study - Building CNN model AlexNet with handwritten digit dataset MNIST 5 5.1. Recurrent Neural Network 5.1.1. Recurrent neural networks – Computational graphs, RNN design 5.1.2. encoder – decoder sequence to sequence architectures 5.1.3. deep recurrent networks, recursive neural networks 5.1.4. modern RNNs LSTM and GRU 5.1.5. Practical use cases for RNNs 5.1.6. Case study - Natural Language Processing SYLLABUS CST395 - NEURAL NETWORKS AND DEEP LEARNING 13
- 14. Date Hour Topic Module Method Of Delivery 2022-10-19 Hour 7 Machine Learning basics - Learning algorithms - Supervised, Unsupervised, Reinforcement, Overfitting, Underfitting, Hyper parameters and Validation sets 1 Lecture 2022-10-21 Hour 3 Estimators -Bias and Variance. Challenges in machine learning 1 Lecture 2022-10-24 Hour 6 Estimators -Bias and Variance. Challenges in machine learning 1 Unknown 2022-10-26 Hour 6 Simple Linear Regression, Logistic Regression 1 Lecture Hour 7 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 Lecture 2022-10-28 Hour 3 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 Unknown 2022-10-31 Hour 6 Introduction to neural networks 2 Lecture 2022-11-02 Hour 6 Single layer perceptrons, Multi Layer Perceptrons (MLPs), Representation Power of MLPs 2 Lecture Hour 7 Single layer perceptrons, Multi Layer Perceptrons (MLPs), Representation Power of MLPs 2 Unknown 2022-11-04 Hour 3 Activation functions - Sigmoid, Tanh, ReLU, Softmax 2 Lecture 2022-11-07 Hour 6 Risk minimization, Loss function 2 Lecture 2022-11-09 Hour 6 Training MLPs with backpropagation 2 Unknown Hour 7 Training MLPs with backpropagation 2 Lecture 2022-11-11 Hour 3 Practical issues in neural network training - The Problem of Overfitting, Vanishing and exploding gradient problems 2 Lecture 2022-11-14 Hour 6 Training MLPs with backpropagation 2 Lecture 2022-11-16 Hour 6 Difficulties in convergence, Local and spurious Optima, Computational Challenges 2 Lecture Hour 7 Applications of neural networks 2 Lecture 2022-11-21 Hour 6 Introduction to deep learning, Deep feed forward network, Training deep models 3 Lecture 2022-11-23 Hour 6 Optimization techniques - Gradient Descent (GD), GD with momentum 3 Lecture 2022-11-28 Hour 6 Nesterov accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam 3 Lecture 2022-11-30 Hour 6 Regularization Techniques - L1 and L2 regularization, Early stopping, Dataset augmentation 3 Lecture 2022-12-02 Hour 3 Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout, Parameter initialization 3 Lecture 2022-12-05 Hour 6 Convolutional Neural Networks – Convolution operation, Motivation, Pooling, Convolution and Pooling as an infinitely strong prior 4 Lecture 2022-12-07 Hour 6 Variants of convolution functions, Structured outputs, Data types 4 Lecture Hour 7 Efficient convolution algorithms 4 Lecture 2022-12-12 Hour 6 Practical use cases for CNNs 4 Lecture 2022-12-14 Hour 6 Case study - Building CNN model AlexNet with handwritten digit dataset MNIST 4 Lecture 2022-12-16 Hour 3 Recurrent neural networks – Computational graphs, RNN design 5 Lecture 2022-12-19 Hour 6 encoder – decoder sequence to sequence architectures 5 Lecture 2022-12-21 Hour 7 deep recurrent networks, recursive neural networks 5 Lecture SUBJECT PLAN CST395 - NEURAL NETWORKS AND DEEP LEARNING 14
- 15. 2022-12-23 Hour 3 modern RNNs LSTM and GRU 5 Lecture 2023-01-04 Hour 6 Practical use cases for RNNs 5 Lecture Hour 7 Case study - Natural Language Processing 5 Lecture Date Hour Topic Module Method Of Delivery CST395 - NEURAL NETWORKS AND DEEP LEARNING 15
- 16. Date Hour Topic Module Remarks 2022-10-14 Hour 3 Machine Learning basics - Learning algorithms - Supervised, Unsupervised, Reinforcement, Overfitting, Underfitting, Hyper parameters and Validation sets 1 2022-10-17 Hour 6 Machine Learning basics - Learning algorithms - Supervised, Unsupervised, Reinforcement, Overfitting, Underfitting, Hyper parameters and Validation sets 1 2022-10-19 Hour 6 Estimators -Bias and Variance. Challenges in machine learning 1 Hour 7 Simple Linear Regression, Logistic Regression 1 2022-10-21 Hour 3 Simple Linear Regression, Logistic Regression 1 2022-10-24 Hour 6 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 2022-10-26 Hour 6 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 Hour 7 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 2022-10-28 Hour 3 Practical use cases for CNNs 4 2022-10-31 Hour 6 Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC) 1 2022-11-02 Hour 6 Training MLPs with backpropagation 2 Hour 7 Introduction to neural networks 2 2022-11-04 Hour 3 Single layer perceptrons, Multi Layer Perceptrons (MLPs), Representation Power of MLPs 2 2022-11-11 Hour 3 Activation functions - Sigmoid, Tanh, ReLU, Softmax 2 2022-11-14 Hour 6 Training MLPs with backpropagation 2 Risk minimization, Loss function 2 2022-11-16 Hour 6 Practical issues in neural network training - The Problem of Overfitting, Vanishing and exploding gradient problems 2 Hour 7 Difficulties in convergence, Local and spurious Optima, Computational Challenges 2 Applications of neural networks 2 2022-11-18 Hour 3 Introduction to deep learning, Deep feed forward network, Training deep models 3 2022-11-21 Hour 6 Convolutional Neural Networks – Convolution operation, Motivation, Pooling, Convolution and Pooling as an infinitely strong prior 4 SUBJECT COVERAGE CST395 - NEURAL NETWORKS AND DEEP LEARNING 16
- 17. S.No Name Type Date Max Mark Result Status Question 1 Series Exam 1 OFFLINE EXAM 23/11/2022 50 Results not published SERIES EXAMS CST395 - NEURAL NETWORKS AND DEEP LEARNING 17
- 18. Total Roll No Name Grace Marks Final Score (out of 50) 1 ABDU NAFIH V P 0 0 4 AJMILA SHADA 0 0 8 AMAL GANESH 0 0 19 ARJUN P JAYAKRISHNAN 0 0 22 ASWANTH K 0 0 26 FAIZAN THEKKE PULUKKOOL 0 0 37 MUHAMMAD ASAD V P 0 0 47 SANDEEP E P 0 0 48 SAYOOJ SATHYAN 0 0 52 TONA ANTONY 0 0 55 VISMAYA B 0 0 64 RAMNATH SHENOY B 0 0 65 ATHUL BENNY 0 0 INTERNAL MARK CST395 - NEURAL NETWORKS AND DEEP LEARNING 18
- 19. Course Exit Survey Details Not Found COURSE EXIT SURVEY CST395 - NEURAL NETWORKS AND DEEP LEARNING 19
- 20. S.No Date Event Remarks YEAR CALENDAR CST395 - NEURAL NETWORKS AND DEEP LEARNING 20
- 21. Roll No Student 14/10 17/10 19/10 21/10 24/10 26/10 28/10 31/10 02/11 04/11 11/11 14/11 16/11 18/11 21/11 Total 3 6 6 7 3 6 6 7 3 6 6 7 3 3 6 6 7 3 6 1 ABDU NAFIH V P P P P P P P P P P P P P P P P P P P P 19/19 (100) 4 AJMILA SHADA P P P P P P P P P P P P P P P P P P P 19/19 (100) 8 AMAL GANESH P P P P P P P P P P P P P P P P P P P 19/19 (100) 19 ARJUN P JAYAKRISHNAN P P P P P P P P P P P P P P P P P P P 19/19 (100) 22 ASWANTH K P P P P P P P P P P P P P P P P P P P 19/19 (100) 26 FAIZAN THEKKE PULUKKOOL P P P P P P P P P P P P P P P P P P P 19/19 (100) 37 MUHAMMAD ASAD V P P P P P P P P P P P P P P P P P P P P 19/19 (100) 47 SANDEEP E P P P P P P P P P P P P P P P P P P P P 19/19 (100) 48 SAYOOJ SATHYAN P P P P P P P P P P P P P P P P P P P 19/19 (100) 52 TONA ANTONY P P P P P P P P P P P P P P P P P P P 19/19 (100) 55 VISMAYA B P P P P P P P P P P P P P P P P P P P 19/19 (100) 64 RAMNATH SHENOY B P P P P P P P P P P P P P P P P P P P 19/19 (100) 65 ATHUL BENNY P P P P P P P P P P P P P P P P P P P 19/19 (100) ATTENDANCE SUMMARY CST395 - NEURAL NETWORKS AND DEEP LEARNING 21
- 22. Roll No Student Total 1 ABDU NAFIH V P 19/19 (100) 4 AJMILA SHADA 19/19 (100) 8 AMAL GANESH 19/19 (100) 19 ARJUN P JAYAKRISHNAN 19/19 (100) 22 ASWANTH K 19/19 (100) 26 FAIZAN THEKKE PULUKKOOL 19/19 (100) 37 MUHAMMAD ASAD V P 19/19 (100) 47 SANDEEP E P 19/19 (100) 48 SAYOOJ SATHYAN 19/19 (100) 52 TONA ANTONY 19/19 (100) 55 VISMAYA B 19/19 (100) 64 RAMNATH SHENOY B 19/19 (100) 65 ATHUL BENNY 19/19 (100) ATTENDANCE SUMMARY CST395 - NEURAL NETWORKS AND DEEP LEARNING 22