Machine Learning
(6CS4-02)
Unit-4 Notes
Vision of the Institute
To become a renowned centre of outcome based learning and work towards
academic, professional, cultural and social enrichment of the lives of individuals
and communities.
Mission of the Institute
M1- Focus on evaluation of learning outcomes and motivate students to
inculcate research aptitude by project based learning.
M2- Identify, based on informed perception of Indian, regional and global
needs, the areas of focus and provide platform to gain knowledge and solutions.
M3- Offer opportunities for interaction between academia and industry.
M4- Develop human potential to its fullest extent so that intellectually capable
and imaginatively gifted leaders can emerge in a range of professions.
Vision of the Department
To become renowned Centre of excellence in computer science and
engineering and make competent engineers & professionals with high ethical
values prepared for lifelong learning.
Mission of the Department
M1-To impart outcome based education for emerging technologies in the field
of computer science and engineering.
M2-To provide opportunities for interaction between academia and industry.
M3- To provide platform for lifelong learning by accepting the change in
technologies
M4- To develop aptitude of fulfilling social responsibilities.
Program Outcomes (PO)
1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the solution of complex
engineering problems.
2. Problem analysis: Identify, formulate, research literature, and analyze complex
engineering problems reaching substantiated conclusions using first principles of
mathematics, natural sciences, and engineering sciences.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities
and norms of the engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or
leader in diverse teams, and in multidisciplinary settings.
10. 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.
11. 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.
12. 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 Educational Objectives (PEO)
1. To provide students with the fundamentals of Engineering Sciences with more
emphasis in Computer Science &Engineering by way of analyzing and exploiting
engineering challenges.
2. To train students with good scientific and engineering knowledge so as to
comprehend, analyze, design, and create novel products and solutions for the real
life problems.
3. To inculcate professional and ethical attitude, effective communication skills,
teamwork skills, multidisciplinary approach, entrepreneurial thinking and an
ability to relate engineering issues with social issues.
4. To provide students with an academic environment aware of excellence,
leadership, written ethical codes and guidelines, and the self-motivated life-long
learning needed for a successful professional career.
5. To prepare students to excel in Industry and Higher education by Educating
Students along with High moral values and Knowledge
Program Specific Outcomes (PSO)
PSO1: Ability to interpret and analyze network specific and cyber security issues, automation
in real word environment.
PSO2: Ability to Design and Develop Mobile and Web-based applications under realistic
constraints.
Course Outcome:
CO1: Understand the concept of machine learning and apply supervised
learning techniques.
CO2: Illustrate various unsupervised leaning algorithm for clustering, and
market basket analysis.
CO3: Analyze statistical learning theory for dimension reduction and model
evaluation in machine learning.
CO4: Apply the concept of semi supervised learning, reinforcement learning and
recommendation system.
CO-PO Mapping:
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
Understand the concept of
machine learning and apply
supervised learning techniques.
3 3 3 3 2 1 1 1 1 2 1 3
Illustrate various unsupervised
leaning algorithm for clustering, 3 3 3 2 2 1 1 1 1 1 1 3
and market basket analysis.
Analyze statistical learning
theory for dimension reduction
and model evaluation in 3 3 3 3 2 2 2 2 1 2 2 3
machine learning.
Apply the concept of semi
supervised learning,
reinforcement learning and
recommendation system. 3 3 3 3 2 1 1 1 1 2 1 3
SYLLABUS:
LECTURE PLAN:
Unit No./
Total Lect. Lect.
Topics
Lecture Reqd. No.
Reqd.
1. Introduction to subject and scope 1 1
2. Introduction to learning, Types of learning and Applications 1 2
3. Supervised Learning 1 3
4. Linear Regression Model 1 4
Unit-I 5. Naïve Bayes Classifier 1 5
(10) 6. Decision Tree 1 6
7. K-nearest Neighbor 1 7
8. Logistic Regression 1 8
9. Support Vector Machine 1 9
10. Random Forest Algorithm 1 10
BC-1 Gradient Descent 1 11
1. Introduction to clustering, K-mean clustering 2 12
2. Hierarchical Clustering 1 14
3. Probabilistic Clustering 1 15
Unit-II
4. Association Rule Mining 1 16
(8)
5. Apriori Algorithm 1 17
6. f-p Growth Algorithm 1 18
7. Gaussian Mixture Model 1 19
1. Feature Extraction- PCA and SVD 3 22
2. Feature Selection- Feature Ranking and Subset Selection 2 24
Unit-III
3. Filter, Wrapper and Embedded Methods 1 25
(8)
4. Evaluating Machine Learning Algorithms 1 26
5. Evaluating Model Selection 1 27
1. Semi supervised learning: Markov Decision Process (MDP) 2 29
2. Bellman Equations 1 30
3. Policy Evaluation using Monte Carlo 1 31
Unit- IV 4. Policy iteration and Value iteration 1 32
(8) 5. Q-Learning 1 33
6. State-Action-Reward-State-Action (SARSA) 1 34
7. Model-based Reinforcement Learning 1 35
1. Recommendation system: Collborative Filtering 1 36
2. Content based filtering 1 37
3. Artificial neural network 1 38
Unit- V
4. Perceptron 1 39
(8)
5. Multilayer network 1 40
6. Backpropagation 1 41
7. Introduction to Deep learning. 2 42
BC-2 Genetic Algorithms 1 44
Text Book:
Machine learning- Tom M Mitchell
